FPT Version Two

Search Algorithm

Author

Jessica Helmer

Published

February 10, 2026

Code

library(tidyverse)

Code

dn_dat_c_full <- readRDS(here::here("Data", "Denominator Neglect", "dn_dat_c.rds"))

bu_dat_h_full <- readRDS(here::here("Data", "Bayesian Update", "bu_dat_h.rds"))

dn_top_items <- readRDS(here::here("Data", "Denominator Neglect", "top_items.rds"))

startup_costs <- readRDS(here::here("Data", "startup_costs.rds"))

Code

get_startup <- function(input_task) {
  startup_costs |>
    filter(task == input_task) |>
    pull(startup)
}

Code

dn_dat_c <- dn_dat_c_full |>
  left_join(dn_top_items |> 
              pivot_longer(c(conf_item, harm_item),
                           names_to = "choice_type", values_to = "item") |>
              select(item, rank),
            by = "item") |>
    mutate(task = "dn.c",
           item = paste0(task, "_", rank),
           # still need time per item
           # currently (time in paper - startup cost) / number of items
           time = (3.43 - get_startup(first(task))) /
             (length(unique(item)) * 2),
           score = correct,
           .keep = "unused") |>
    select(subject_id, sscore, task, item, score, time)

Code

bu_dat_h <- bu_dat_h_full |>
    mutate(task = "bu.h",
           item = paste0(task, "_", unique_trial),
           time = (6.56 - get_startup(first(task))) / length(unique(item)),
           .keep = "unused") |>
    select(subject_id, sscore, task, item, score, time)

Code

v2_dat <- rbind(dn_dat_c, 
                bu_dat_h)

Search Algorithm: FPT Version 2

The FPT Version 2 includes two cognitive tasks.

Denominator Neglect: Combined
Bayesian Update: Hard

Methods

This algorithm seeks to maximize the \(R^2\) of predicting s-scores per minute for a set of cognitive task items of average testing time t.

For all items of the FPT Version 2 cognitive tasks, calculate the \(R^2\) of predicting s-scores of each single item.
Divide that \(R^2\) by the average time it takes to complete an item of that task.
Identify the item with the maximum \(R^2\) per testing time.
Add that item to the test.
- Include information of the the task, item number, its \(R^2\), and its average testing time.
For all remaining items, calculate the \(R^2\) of a test containing that item and the already included item with s-scores.
- Within this test, average the score within task and then within person to get a person score.
Divide that \(R^2\) by the average time it would take to complete the total proposed test.
Identify the proposed test with the maximum \(R^2\) per testing time.
Continue until the testing time reaches the desired t.

Notes:

For Denominator Neglect, items need to be considered as conflict-harmony pairs to ensure an equal number of conflict and harmony items land on the test.

To manage individual task needs, it may be helpful to identify the maximum correlation per testing time within each task and then compare across tasks. I don’t think that will add to any runtime because it will be the same total number of comparisons.

This means for Denominator Neglect, we can either calculate the correlations across all pairs of possible conflict-harmony items, or we could refer to the top Denominator Neglect items list per the IRT results, and take those as the pairs. To save a little runtime, I’m going to use the pairs list to set the pairs at least to start.

Code

# this is copied from Cleaning until i find a more elegant solution
dn_dat_c <- dn_dat_c |>
  left_join(dn_top_items |> 
              pivot_longer(c(conf_item, harm_item),
                           names_to = "choice_type", values_to = "item") |>
              select(item, rank),
            by = "item") |>
    mutate(task = "dn.c",
           item = paste0(task, "_", rank),
           time = 3.43 / length(unique(item)),
           score = correct,
           .keep = "unused") |>
    select(subject_id, sscore, task, item, score, time)

Task: initialize test dataframe.

Code

test <- data.frame(task = as.character(),
                   item = as.character(),
                   test_r2 = as.numeric(),
                   time = as.numeric(),
                   r2_rate = as.numeric())

Task: for Denominator Neglect, identify the \(R^2\) predicting s-scores for each item pair. And their rates per minute.

Code

dn_r2s <- dn_dat_c |>
  summarize(.by = c(subject_id, item),
            score = mean(score),
            sscore = first(sscore),
            time = sum(time),
            task = first(task)) |>
  drop_na() |>
  summarize(.by = item,
            task = first(task),
            test_r2 = summary(lm(sscore ~ score, data = data.frame(sscore, score)))$r.squared,
            time = first(time),
            r2_rate = test_r2 / first(time)) |>
  arrange(desc(r2_rate))
dn_r2s

      item task    test_r2       time   r2_rate
1   dn.c_1 dn.c 0.16864766 0.08872847 1.9007164
2  dn.c_13 dn.c 0.14667889 0.08872847 1.6531209
3  dn.c_10 dn.c 0.14246156 0.08872847 1.6055902
4   dn.c_5 dn.c 0.13415138 0.08872847 1.5119316
5   dn.c_6 dn.c 0.13093856 0.08872847 1.4757220
6  dn.c_19 dn.c 0.12985682 0.08872847 1.4635304
7  dn.c_16 dn.c 0.12976019 0.08872847 1.4624414
8   dn.c_4 dn.c 0.12917141 0.08872847 1.4558057
9  dn.c_12 dn.c 0.12793298 0.08872847 1.4418481
10 dn.c_14 dn.c 0.12111792 0.08872847 1.3650401
11  dn.c_7 dn.c 0.11802329 0.08872847 1.3301626
12 dn.c_11 dn.c 0.11442960 0.08872847 1.2896604
13  dn.c_2 dn.c 0.11293893 0.08872847 1.2728600
14 dn.c_21 dn.c 0.11197012 0.08872847 1.2619412
15  dn.c_9 dn.c 0.10782963 0.08872847 1.2152766
16 dn.c_15 dn.c 0.10636893 0.08872847 1.1988140
17 dn.c_23 dn.c 0.10293338 0.08872847 1.1600941
18  dn.c_8 dn.c 0.10285300 0.08872847 1.1591882
19 dn.c_24 dn.c 0.09667912 0.08872847 1.0896065
20  dn.c_3 dn.c 0.09312134 0.08872847 1.0495091
21 dn.c_20 dn.c 0.09292729 0.08872847 1.0473222
22 dn.c_17 dn.c 0.09174215 0.08872847 1.0339652
23 dn.c_18 dn.c 0.08094097 0.08872847 0.9122322
24 dn.c_22 dn.c 0.07829387 0.08872847 0.8823985

Task: identify the item with the highest \(R^2\) rate.

Code

dn_max_r2 <- dn_r2s |>
  filter(r2_rate == max(r2_rate))

Task: for Bayesian Update, identify the \(R^2\) predicting s-scores for each item. And their rates per minute.

Code

bu_r2s <- bu_dat_h |>
  
  # this summarize() doesn't actually do anything for BU
  summarize(.by = c(subject_id, item),
            score = mean(score),
            sscore = first(sscore),
            time = sum(time),
            task = first(task)) |>
  drop_na() |>
  summarize(.by = item,
            task = first(task),
            test_r2 = summary(lm(sscore ~ score, data = data.frame(sscore, score)))$r.squared,
            time = first(time),
            r2_rate = test_r2 / first(time)) |>
  arrange(desc(r2_rate))
bu_r2s

    item task   test_r2     time   r2_rate
1 bu.h_6 bu.h 0.1582450 0.506451 0.3124586
2 bu.h_2 bu.h 0.1488402 0.506451 0.2938887
3 bu.h_7 bu.h 0.1367507 0.506451 0.2700176
4 bu.h_3 bu.h 0.1323354 0.506451 0.2612995
5 bu.h_4 bu.h 0.1312685 0.506451 0.2591929
6 bu.h_5 bu.h 0.1280508 0.506451 0.2528394
7 bu.h_1 bu.h 0.1274959 0.506451 0.2517439
8 bu.h_0 bu.h 0.1249573 0.506451 0.2467313

Task: identify the item with the highest \(R^2\)

Code

bu_max_r2 <- bu_r2s |>
  filter(r2_rate == max(r2_rate))

Task: of all tasks, identify the item with the highest \(R^2\)

Code

max_r2 <- rbind(dn_max_r2,
      bu_max_r2) |>
  filter(r2_rate == max(r2_rate))
max_r2

    item task   test_r2       time  r2_rate
1 dn.c_1 dn.c 0.1686477 0.08872847 1.900716

Task: add that item to the test

Code

test <- rbind(test, max_r2)

Now comes the harder part. Need to generalize this to working with (1) already having items within test and (2) each linear model including each task separately.

Starting with Bayesian Update because right away will need separate terms in the linear model.

Task: for Bayesian Update, identify the \(R^2\) predicting s-scores for each item. And their rates per minute. Include the currently included items. Okay now its looking like a combined dataframe would be helpful. Joining every rep seems ridiculous. Or maybe I should just join in the set test. I’m convinced we should have a combined set. Not sure if we should only have combined, but I’m going to adjust up above.

Code

map(bu_dat_h |> pull(item) |> unique(), 
    ~ rbind(bu_dat_h |>
        filter(item == .x),
      v2_dat |>
        filter(item %in% pull(test, item))) |> 
  summarize(.by = c(task, subject_id),
            score = mean(score),
            sscore = first(sscore),
            time = sum(time),
            task = first(task)) |>
  mutate(.by = subject_id,
         time = sum(time)) |>
  pivot_wider(names_from = task, values_from = score) |>
  summarize(task = str_split_i(.x, "_", 1),
            item = .x,
            test_r2 = summary(lm(sscore ~ dn.c + bu.h,
                                 data = data.frame(sscore, dn.c, bu.h)))$r.squared,
            time = first(time),
            r2_rate = test_r2 / first(time))) |>
  list_rbind() |>
  arrange(desc(r2_rate))

# A tibble: 8 × 5
  task  item   test_r2  time r2_rate
  <chr> <chr>    <dbl> <dbl>   <dbl>
1 bu.h  bu.h_6   0.262 0.595   0.440
2 bu.h  bu.h_2   0.248 0.595   0.417
3 bu.h  bu.h_7   0.246 0.595   0.414
4 bu.h  bu.h_5   0.244 0.595   0.409
5 bu.h  bu.h_1   0.240 0.595   0.402
6 bu.h  bu.h_0   0.238 0.595   0.400
7 bu.h  bu.h_4   0.238 0.595   0.399
8 bu.h  bu.h_3   0.237 0.595   0.398

Okay, now I’d like to iterate over trying both Bayesian Update items AND Denominator Neglect items. I think I need to change the linear model call so it only includes predictors that are in the data. This could probably be an if thing, but I could also do some dynamic formulas. Let’s work on it.

Code

v2_dat |> 
  filter(!(item %in% pull(test, item))) |>
  pull(item) |>
  unique() |> 
  map(~ rbind(v2_dat |>
                filter(item == .x),
              v2_dat |>
                filter(item %in% pull(test, item))) |> 
        summarize(.by = c(task, subject_id),
                  score = mean(score),
                  sscore = first(sscore),
                  time = sum(time),
                  task = first(task)) |>
        mutate(.by = subject_id,
               time = sum(time)) |> 
        mutate(formula = paste("sscore ~", task |> unique() |> paste(collapse = "+"))) |>
        pivot_wider(names_from = task, values_from = score) |>
        summarize(task = str_split_i(.x, "_", 1),
                  item = .x,
                  test_r2 = summary(lm(as.formula(first(formula)),
                                       data = cur_data()))$r.squared,
                  time = first(time),
                  r2_rate = test_r2 / first(time))) |>
  list_rbind() |>
  arrange(desc(r2_rate))

Warning: There was 1 warning in `summarize()`.
ℹ In argument: `test_r2 = summary(lm(as.formula(first(formula)), data =
  cur_data()))$r.squared`.
Caused by warning:
! `cur_data()` was deprecated in dplyr 1.1.0.
ℹ Please use `pick()` instead.

# A tibble: 31 × 5
   task  item    test_r2  time r2_rate
   <chr> <chr>     <dbl> <dbl>   <dbl>
 1 dn.c  dn.c_13   0.242 0.177    1.36
 2 dn.c  dn.c_16   0.237 0.177    1.34
 3 dn.c  dn.c_14   0.233 0.177    1.32
 4 dn.c  dn.c_6    0.231 0.177    1.30
 5 dn.c  dn.c_10   0.230 0.177    1.30
 6 dn.c  dn.c_5    0.229 0.177    1.29
 7 dn.c  dn.c_2    0.226 0.177    1.27
 8 dn.c  dn.c_19   0.224 0.177    1.27
 9 dn.c  dn.c_12   0.216 0.177    1.22
10 dn.c  dn.c_11   0.216 0.177    1.22
# ℹ 21 more rows

Great. We are able to get out the \(R^2\) rate for all items after one item is in the test. And, this works if no items are in the test too.

Code

test <- data.frame(task = as.character(),
                   item = as.character(),
                   test_r2 = as.numeric(),
                   time = as.numeric(),
                   r2_rate = as.numeric())

v2_dat |> 
  filter(!(item %in% pull(test, item))) |>
  pull(item) |>
  unique() |> 
  map(~ rbind(v2_dat |>
                filter(item == .x),
              v2_dat |>
                filter(item %in% pull(test, item))) |> 
        summarize(.by = c(task, subject_id),
                  score = mean(score),
                  sscore = first(sscore),
                  time = sum(time),
                  task = first(task)) |>
        mutate(.by = subject_id,
               time = sum(time)) |> 
        mutate(formula = paste("sscore ~", task |> unique() |> paste(collapse = "+"))) |>
        pivot_wider(names_from = task, values_from = score) |>
        summarize(task = str_split_i(.x, "_", 1),
                  item = .x,
                  test_r2 = summary(lm(as.formula(first(formula)),
                                       data = cur_data()))$r.squared,
                  time = first(time),
                  r2_rate = test_r2 / first(time))) |>
  list_rbind() |>
  arrange(desc(r2_rate))

# A tibble: 32 × 5
   task  item    test_r2   time r2_rate
   <chr> <chr>     <dbl>  <dbl>   <dbl>
 1 dn.c  dn.c_1    0.169 0.0887    1.90
 2 dn.c  dn.c_13   0.147 0.0887    1.65
 3 dn.c  dn.c_10   0.142 0.0887    1.61
 4 dn.c  dn.c_5    0.134 0.0887    1.51
 5 dn.c  dn.c_6    0.131 0.0887    1.48
 6 dn.c  dn.c_19   0.130 0.0887    1.46
 7 dn.c  dn.c_16   0.130 0.0887    1.46
 8 dn.c  dn.c_4    0.129 0.0887    1.46
 9 dn.c  dn.c_12   0.128 0.0887    1.44
10 dn.c  dn.c_14   0.121 0.0887    1.37
# ℹ 22 more rows

So now, we can start putting together this phrase that finds the \(R^2\) rates and adding them to the test. Instead of getting a dataframe of all the items’ \(R^2\) rate, I’d like to start getting back the test itself as items get added.

Code

test <- data.frame(i = as.numeric(),
                   task = as.character(),
                   item = as.character(),
                   test_r2 = as.numeric(),
                   time = as.numeric(),
                   r2_rate = as.numeric())

repeat {
  test <- v2_dat |> 
    filter(!(item %in% pull(test, item))) |>
    pull(item) |>
    unique() |> 
    map(~ rbind(v2_dat |>
                  filter(item == .x),
                v2_dat |>
                  filter(item %in% pull(test, item))) |> 
          summarize(.by = c(task, subject_id),
                    score = mean(score),
                    sscore = first(sscore),
                    time = sum(time),
                    task = first(task)) |>
          mutate(.by = subject_id,
                 time = sum(time)) |> 
          mutate(formula = paste("sscore ~", task |> unique() |> paste(collapse = "+"))) |>
          pivot_wider(names_from = task, values_from = score) |>
          summarize(i = nrow(test) + 1,
                    task = str_split_i(.x, "_", 1),
                    item = .x,
                    test_r2 = summary(lm(as.formula(first(formula)),
                                         data = cur_data()))$r.squared,
                    time = first(time),
                    r2_rate = test_r2 / first(time))) |>
    list_rbind() |>
    filter(r2_rate == max(r2_rate)) |>
    rbind(test)
  
  if (test |> pull(time) |> max() > 1) break
}

Ok cool. So that can get us the items that would be included in a one-minute test by optimizing for \(R^2\) rate per minute in a linear model assessing items across Denominator Neglect and Bayesian Update. Looks like only Denominator Neglect makes the cut right now.

But really, I don’t know how valuable it is to keep it like that as time-based. I know I said I would do that in the setup, but wouldn’t it be better to just run it across all items and get the time / \(R^2\) curve? Then we could always just look at desired time cutoffs.

Is this stepwise strategy better than an all-combinations one? Probably not.

Anyway. Let’s get this for all items. I’ll just have it iterate over integers through the total potential number of items.

Code

test <- data.frame(i = as.numeric(),
                   task = as.character(),
                   item = as.character(),
                   test_r2 = as.numeric(),
                   time = as.numeric(),
                   r2_rate = as.numeric())

walk(seq(1, v2_dat |> pull(item) |> unique() |> length()), 
     \(i) {test <<- v2_dat |> 
       filter(!(item %in% pull(test, item))) |>
       pull(item) |>
       unique() |> 
       map(~ rbind(v2_dat |>
                     filter(item == .x),
                   v2_dat |>
                     filter(item %in% pull(test, item))) |> 
             summarize(.by = c(task, subject_id),
                       score = mean(score),
                       sscore = first(sscore),
                       time = sum(time),
                       task = first(task)) |>
             mutate(.by = subject_id,
                    time = sum(time)) |> 
             mutate(formula = paste("sscore ~", task |> unique() |> paste(collapse = "+"))) |>
             pivot_wider(names_from = task, values_from = score) |>
             summarize(i = i,
                       task = str_split_i(.x, "_", 1),
                       item = .x,
                       test_r2 = summary(lm(as.formula(first(formula)),
                                            data = cur_data()))$r.squared,
                       time = first(time),
                       r2_rate = test_r2 / first(time))) |>
       list_rbind() |>
       filter(r2_rate == max(r2_rate)) |>
       rbind(test)}, 
     .progress = T)
test

Code

test <- data.frame(rep = 0,
                   task = "INIT",
                   item = "INIT",
                   test_r2 = 0,
                   time = 0,
                   added_r2 = 0,
                   added_time = 0,
                   r2_rate = 0)

walk(seq(1, v2_dat |> pull(item) |> unique() |> length()), 
     \(i) {test <<- v2_dat |> 
       # get just those items not in the test (run through everything left)
       filter(!(item %in% pull(test, item))) |>
       pull(item) |>
       unique() |> 
       map(~ rbind(v2_dat |>
                     # just the data from the tested item
                     filter(item == .x),
                   v2_dat |>
                     # just the data from the existing test
                     filter(item %in% pull(test, item))) |>
             # get task-specific scores, sum task time
             summarize(.by = c(task, subject_id),
                       score = mean(score),
                       sscore = first(sscore),
                       time = sum(time),
                       task = first(task)) |>
             mutate(time = time + map_dbl(task, get_startup)) |>
             # keeping track of the total time (repeated on all rows)
             mutate(.by = subject_id,
                    time = sum(time)) |> 
             # building model formula string of just those tasks already included
             mutate(formula = paste("sscore ~", task |> unique() |> paste(collapse = "+"))) |>
             # need task data in separate columns for lm()
             pivot_wider(names_from = task, values_from = score) |>
             summarize(rep = i,
                       # extract task name from item name
                       task = str_split_i(.x, "_", 1),
                       item = .x,
                       # extract R^2 (repeated on all rows)
                       test_r2 = summary(lm(as.formula(first(formula)),
                                            data = cur_data()))$r.squared,
                       # get increase in R^2
                       added_r2 = first(test_r2) - test |>
                         head(1) |>
                         pull(test_r2),
                       # get increase in time
                       added_time = first(time) - test |>
                         head(1) |>
                         pull(time),
                       time = first(time),
                       r2_rate = first(added_r2 / added_time))) |>
       list_rbind() |>
       filter(r2_rate == max(r2_rate)) |>
       rbind(test) |>
       filter(item != "INIT")}, 
     .progress = T)
test

saveRDS(test, here::here("Data", "Search Algorithms", "v2test_dat.rds"))

Code

test <- readRDS(here::here("Data", "Search Algorithms", "v2test_dat.rds"))

Code

test |>
  ggplot(aes(x = time, y = test_r2)) +
  geom_line() +
  geom_point(aes(color = task), size = 2) +
  theme_classic() +
  guides(x = guide_axis(cap = "both"),
         y = guide_axis(cap = "both"))

Idk why:

Code

v2_dat |>
  summarize(.by = c(subject_id, task),
            score = mean(score))

               subject_id task       score
1    6UwmqiWhZgQPMStTzRH0 dn.c 0.854166667
2    FCnbdviPzU6P89xbfmi6 dn.c 0.541666667
3    FWjyJsu6QVJLpQGvz0xK dn.c 1.000000000
4    5DL0xvNKFcTIsyhiIrAL dn.c 0.708333333
5    DXeOGqQEgoEt16VOuaOn dn.c 0.375000000
6    Qtvbbq7TeGc36LLpTwWm dn.c 0.979166667
7    zE4Nw3teBP9xAkpDgZpA dn.c 0.500000000
8    cRpM8Z159NED2UYcb3jT dn.c 0.625000000
9    qexjC6y4MMUWHAt5bqvh dn.c 0.854166667
10   4otGofdOW76kH63oYmSe dn.c 0.708333333
11   Fvbgz7h6itCmDDBRtsYh dn.c 0.958333333
12   0gDNwt5z4VUntFijldez dn.c 0.770833333
13   qEVxqrorFuK0BCpZbUoy dn.c 0.791666667
14   hmUKEUxhdCzEzo2q7PpI dn.c 0.750000000
15   BvKCMACzXtxloOwf0FNW dn.c 0.666666667
16   SXkZQJ4KsTJedVvYz11o dn.c 0.833333333
17   NxDOJ5WGiu7vMzc0jfg5 dn.c 0.541666667
18   Q62Nhaqimia3RQ8wxRR6 dn.c 0.833333333
19   Q5v7vEQrx6bW7SNYTjUD dn.c 0.791666667
20   WLWN8Q08fR7kHvqx8cYI dn.c 0.937500000
21   UrLHzDA28PhC94FqCbUB dn.c 1.000000000
22   Ac5ZMy5LVkFjQHr2ucen dn.c          NA
23   YxPY8EcFK12BGCSzOjD8 dn.c 0.645833333
24   Nhfi0ZY8Ti7wJnO4nIYo dn.c 0.541666667
25   HqfnXYULelL4tEtMA05t dn.c 0.500000000
26   xerEAhCM3QCii740dMOT dn.c          NA
27   nmy4BvIJvHgW6RKgnx5i dn.c          NA
28   vLZOQcg7ef2x9mZXoFxr dn.c 0.583333333
29   n67Cb0LnyydD86IYfON7 dn.c 0.604166667
30   lVf9yb3rd8R5jMUeXTP0 dn.c 0.625000000
31   aEalfO2zYVQmNGz6LclE dn.c 0.500000000
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901  j1lSAtVBMhimMpC1Ha2o dn.c 0.979166667
902  ur0bltcfUPDHCL7htFAq dn.c 0.958333333
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911  wTvMiG16cCRbflcLBehg dn.c 0.729166667
912  xDJ0JpfeWhke1GvUwHem dn.c          NA
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914  0vQoOOqbZMP3otq0XdM9 dn.c 0.604166667
915  BlbFlr57Zxe7vC1FZ2xu dn.c 0.520833333
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920  mgLxjjWNdkpi3rxUB07l dn.c 0.458333333
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923  MbyIbNiCdxnuVxUZpfbH dn.c 0.937500000
924  O72SSmqP7lQY9cI1IRXS dn.c 0.645833333
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951  TJQ9j6A05f8ld8OK8Mzc dn.c 0.708333333
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956  QTIBd0LPhjpEAg2t8jUc dn.c 0.895833333
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959  EgcARy4I4dqxRFy1uljh dn.c 0.812500000
960  3lGsczpaOxmDeZmuT7yz dn.c 1.000000000
961  7AlcRBY2aNYzLP9XgiiD dn.c 0.750000000
962  nT7TY7TedDb0ioGYwJu2 dn.c 0.979166667
963  ifcG7cR8IYUd48e11ERX dn.c          NA
964  xVa4sxiVQcwRAFeHBw6Q dn.c 0.687500000
965  piTJXDJA6kamnbAqQVSc dn.c 0.562500000
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987  7prg1cG0rIH53Z3B960Y dn.c 0.875000000
988  hf6J6yzRivfwR2Elyo9N dn.c          NA
989  XGaVXviFC59TLyx7AKLq dn.c 0.687500000
990  N8mhQdi0Ckv2LWNLk81f dn.c 0.625000000
991  5PP2t16GSzChbtte5vVw dn.c 0.500000000
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996  ZWrbw4NnMzB5thxdUE70 dn.c 0.479166667
997  za6MrfAimgv3r4mlD3GD dn.c 0.979166667
998  FbszVu1NkNjugVYIkYSq dn.c 0.937500000
999  RIcuhrLt8BIs1le0DQl9 dn.c 0.645833333
1000 f0Qhy4BBQMm7haZW9njV dn.c 0.416666667
1001 U3xR5fFywsrVpOBA8QLl dn.c 0.895833333
1002 ghnnf7NOCbptKwZqbJP0 dn.c 0.604166667
1003 HySpCCsc6Z24bjQNY5WL dn.c 0.979166667
1004 Y4piZj3qKcsxJWYZlPUu dn.c 0.791666667
1005 w5ROjuO9oQmzFgChYwnN dn.c 0.541666667
1006 kovEJOLHZyADSgwnqPga dn.c 0.479166667
1007 ZafHADISIiTJh88ymVWB dn.c 0.479166667
1008 NneQhA1SAtPySGJTD5kT dn.c 0.979166667
1009 EsnHWHltxFFWBvnaQ7wt dn.c 0.958333333
1010 MF7ywseHC6Lpx2uLgz7R dn.c 0.708333333
1011 MJzHLwX8uDBLf5PVpvm3 dn.c 0.500000000
1012 WvS7v3OnokNVBRlGLawD dn.c 1.000000000
1013 gMVww8j5DaVLpC3Yj8nr dn.c 0.312500000
1014 ZyEbe7eQxt4YlalJLb89 dn.c 0.666666667
1015 LGc1FR04HUEsBPr46ymO dn.c 0.666666667
1016 ZcTx9ee6nFJHzT0GdDGB dn.c 0.958333333
1017 Q6i1DNEvpyBBDntIgDg4 dn.c 0.791666667
1018 sPOsOxzbf57Cpl4JzYRp dn.c 1.000000000
1019 oy61qtPF4pg4R3ABVbJj dn.c 0.500000000
1020 LxtXIECwYcSxnNetzXg7 dn.c 0.812500000
1021 TnkZ3dTyO65eSD5NNCjV dn.c 0.958333333
1022 5YbhiAWE4VtpOkgYKiOV dn.c 0.645833333
1023 5a0sZZG2EX1SidVr8pTr dn.c 0.541666667
1024 BgX9DZHT7atuF2UAoakM dn.c 0.770833333
1025 kOdPwuboub15eEeAr6EV dn.c 0.645833333
1026 XcEGK0YT24vUTt758M2g dn.c 0.520833333
1027 0REiwYHX47zX4qbePYjN dn.c          NA
1028 vt916IZuBW8BbCO8LZhg dn.c 0.583333333
1029 CbkDGfCW3DtUwMDx16uM dn.c 0.500000000
1030 6d6gzVA98GwteUHuM56j dn.c 0.875000000
1031 UHNhZWUf2dWUy4LM6hpE dn.c 0.708333333
1032 RoIHSTba58R45QQst0Ei dn.c 0.979166667
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1036 8FYM23YbIrj3Ofx7LMoo dn.c 0.791666667
1037 9pEwavS3W13KgQTb81Oi dn.c 0.687500000
1038 ym15ZZj8Df0uNAuodXn8 dn.c 0.979166667
1039 82Zq2FjvSCEGrSz97ocz dn.c 0.520833333
1040 XZ6kYLrylUTUVmi9Moii dn.c 0.604166667
1041 jnddVA5OnRXjgyBHLhkr dn.c 0.458333333
1042 DH0T6zFub589qUF0B702 dn.c 0.791666667
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1044 7g48cSLs8jYPIKalXiUZ dn.c 0.520833333
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1046 wQtadQgeGBUtR1EtHfet dn.c 0.583333333
1047 Omi5XdQydF17Y0ds4gmO dn.c 0.729166667
1048 ia068eboWvrxspWSSUh4 dn.c 1.000000000
1049 SNsBrz8Txh3QpNgGXGtE dn.c          NA
1050 9YaeRGOTkxRml6BYldlV dn.c 0.666666667
1051 qy5e2BKyVAdHLE9Ft3KP dn.c 0.979166667
1052 wWq9wLHxof075cOrUFE6 dn.c 0.625000000
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1054 B5xt3fvqtOgMSQdQDwRx dn.c 0.854166667
1055 3LlGLQjPsYvg4P5YSea5 dn.c 0.625000000
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1057 yceiAnl0ceWjKibWwUt8 dn.c 1.000000000
1058 syaOSO9GuFLWojMPYoT5 dn.c 0.520833333
1059 tWVab4P75BG6pBVotEpy dn.c 0.937500000
1060 n76EcPNePczVQ85qQtbB dn.c 0.687500000
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1063 sDpNHlYjovuecpifbRww dn.c 0.520833333
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1068 Lln9o6Ma46KYfVigvXR8 dn.c 0.687500000
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1075 PHhwWi1zxc7KS7rrgWg9 dn.c 0.729166667
1076 tGZegyyiWppuB0tUkXWz dn.c 0.687500000
1077 OcE6oQcbHzuzftyyrdB6 dn.c 0.895833333
1078 jreiAkqFiypqzeLGlilx dn.c 0.875000000
1079 Qr9Jo7wwBKk9VcRGOXsQ dn.c 0.541666667
1080 SA6j8d7euDro6XzatjNQ dn.c 0.500000000
1081 XYf3SepcvILnlDs8WTBG dn.c 0.854166667
1082 xgX9LZbxnEJvwHKfw0AT dn.c 0.520833333
1083 1E9KZrTTpuJ3KjJa8cDV dn.c          NA
1084 mlhVFjOIFIcEw15BvvUI dn.c 0.958333333
1085 ZXZt7x4rQtyCuV68Yi6b dn.c 0.979166667
1086 pFp0W0oC9Ym2PNt90vzA dn.c 0.854166667
1087 jtLHDBCHTQmFdAKk960m dn.c 0.979166667
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1089 qcx2RIuXihoDjqT84xfl dn.c 1.000000000
1090 5Jqzges6X72YJR7IVU7C dn.c 1.000000000
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1094 54TEF4eZN23KZglG8kHT dn.c 0.625000000
1095 z8N4Pa7qb9TeTYKUwAeQ dn.c 0.541666667
1096 tRvesqPE5A0iKVY6G8g8 dn.c          NA
1097 VpHBzD0cNZ0G63Z3WZbn dn.c 0.625000000
1098 M0Uj6L1r76s383Di6apI dn.c 0.708333333
1099 psLDhepHETw4nS6WsOGq dn.c 0.958333333
1100 21eJs1O3lWsEI15RTa48 dn.c 0.437500000
1101 8JPlYoclZmsdBMbSSwUV dn.c 1.000000000
1102 g3iCPU6zUjzgEz6cwB61 dn.c 0.979166667
1103 9aDaI43JOLXyWCFRzVo5 dn.c 0.520833333
1104 6uMXuuY18it6mt78sREJ dn.c 0.937500000
1105 A4b1mpsMcrAeuvFB9ZEM dn.c 0.645833333
1106 R1sGzUZNWuEyAXcRhhWb dn.c 0.979166667
1107 LobHXXnfhsExJHBeznFT dn.c 0.958333333
1108 IB0qJtGPs6xL8JRpYuBt dn.c 0.979166667
1109 GexzYjhJP1cjPlyIYmBj dn.c 0.770833333
1110 jmSta4zKv7uVTnV75Qym dn.c 1.000000000
1111 IwtSIMOyuZcrJJ2dSide dn.c 0.979166667
1112 uQsTKluXIBBL8fQW9XnR dn.c 0.979166667
1113 r0J8vYU2GVNM7AaMntD4 dn.c 0.687500000
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1115 MsFmP6wdnCIMDOShVnvZ dn.c 0.833333333
1116 Yjyw7yxk1JfumMK3kuLP dn.c 0.541666667
1117 076BrNISK0IP8PsESVQX dn.c 1.000000000
1118 713sXb02mkrHAlE48iCz dn.c 0.770833333
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1120 roz2c3opqAFnyNPbzR3h dn.c 0.979166667
1121 srkAQIQzLKHWoGWrdXiz dn.c 1.000000000
1122 uFa2AxLMcTcWQza7yFWZ dn.c 0.666666667
1123 j3wPPP2j4S3VgnpmBFvH dn.c 0.500000000
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1125 CW8zKiKJE434idpLxkzM dn.c 0.645833333
1126 SnRg4VW74nsig9pvR2r9 dn.c 0.937500000
1127 UTVQBHU3VaGvVYCLcwsS dn.c 0.958333333
1128 qUSXrRXrb45EnRXm0CZK dn.c 0.979166667
1129 cEus0bkT55lQZjQ0kWT5 dn.c 0.583333333
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1131 zoxX2w6t4KHJ9jlRK4GI dn.c 0.645833333
1132 rfhrXlAv1kmfzwFzjCuj dn.c 0.937500000
1133 6Bnbr5wfMsSoEwr4RmmY dn.c 1.000000000
1134 bjusHaMvMJEVS8XmZ80t dn.c 0.687500000
1135 bKtiD8u9UlxKRlobK18T dn.c 0.500000000
1136 wj0wxrv82fTB9XrX8A3i dn.c 0.583333333
1137 Aqp9HI94q4KffiYZI489 dn.c 0.979166667
1138 b8M0JyK1URtbzEoC6nsg dn.c 0.750000000
1139 lTh2mTi7deayrBw0FqFF dn.c 0.583333333
1140 mgH3X8YHuyVo5NqXdSaj dn.c 0.875000000
1141 cGWuqesye3WK7ieL0ifh dn.c 0.500000000
1142 swOv8V46B4RZrXYTCYCb dn.c 0.604166667
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1144 BWrEZMl6F6fPf8Cfark7 dn.c 0.541666667
1145 1wKWKuWmqqmvNAmw1RqQ dn.c 0.666666667
1146 dFi2sGOS1gEWWg2KDXcs dn.c 0.562500000
1147 fGcn13Dce53Ic5t7KUSy dn.c 0.895833333
1148 I39nu6yFKdVI2LWsWJc2 dn.c 0.666666667
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1150 w1mlcyU3jmDPqAIXSuV2 dn.c 0.770833333
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1153 9eUKJ1MDRhFJ0kdt2ikq dn.c 0.958333333
1154 fRN2BqpepgaxT82ExHgD dn.c 0.770833333
1155 JwtAapnTB0iT2CwsIkL0 dn.c 0.541666667
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1158 puSjjud5MPEJwWtnleFz dn.c 1.000000000
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1161 FMzsqWwp0Rj2W65ySPuI dn.c 0.958333333
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1167 g3iMJxodMUlpO2mQ8tOt dn.c 0.500000000
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1177 z0vuKL7cyHyK1USkklpb dn.c 0.520833333
1178 fZqqnVNdLD3dJmD2f5nj dn.c 0.541666667
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1187 5DL0xvNKFcTIsyhiIrAL bu.h 0.361721922
1188 DXeOGqQEgoEt16VOuaOn bu.h          NA
1189 Qtvbbq7TeGc36LLpTwWm bu.h 0.133773504
1190 zE4Nw3teBP9xAkpDgZpA bu.h 0.399827257
1191 cRpM8Z159NED2UYcb3jT bu.h 0.279663278
1192 qexjC6y4MMUWHAt5bqvh bu.h 0.401050799
1193 4otGofdOW76kH63oYmSe bu.h 0.189820333
1194 Fvbgz7h6itCmDDBRtsYh bu.h 0.504816738
1195 0gDNwt5z4VUntFijldez bu.h 1.442287335
1196 qEVxqrorFuK0BCpZbUoy bu.h 0.355273499
1197 hmUKEUxhdCzEzo2q7PpI bu.h 0.657140429
1198 BvKCMACzXtxloOwf0FNW bu.h 0.187988597
1199 SXkZQJ4KsTJedVvYz11o bu.h 0.230141349
1200 NxDOJ5WGiu7vMzc0jfg5 bu.h 0.296971706
1201 Q62Nhaqimia3RQ8wxRR6 bu.h 0.211488347
1202 Q5v7vEQrx6bW7SNYTjUD bu.h 0.496590819
1203 WLWN8Q08fR7kHvqx8cYI bu.h 0.135819809
1204 Ac5ZMy5LVkFjQHr2ucen bu.h          NA
1205 YxPY8EcFK12BGCSzOjD8 bu.h 2.495491630
1206 Nhfi0ZY8Ti7wJnO4nIYo bu.h 0.481086709
1207 HqfnXYULelL4tEtMA05t bu.h          NA
1208 xerEAhCM3QCii740dMOT bu.h 0.619505596
1209 nmy4BvIJvHgW6RKgnx5i bu.h 0.179058409
1210 vLZOQcg7ef2x9mZXoFxr bu.h 0.504642238
1211 n67Cb0LnyydD86IYfON7 bu.h 1.377718055
1212 lVf9yb3rd8R5jMUeXTP0 bu.h 0.943723381
1213 aEalfO2zYVQmNGz6LclE bu.h 0.299694578
1214 agKk7RbWzbO8PrWqXEW0 bu.h 0.415362016
1215 VEXHf6Q3dSG6vKlcvhzj bu.h 0.313643440
1216 CPwdHnAnqZuJJEvQdgWC bu.h          NA
1217 DkhleOQUYKN0Tj5WLg0n bu.h 0.159050919
1218 uRnLhR9EHf3hyvc8IAhA bu.h 0.265340617
1219 yL2xNKGqpeaOzg95CDUB bu.h 0.454889278
1220 r8w1FvRydOvSvSWrVsy4 bu.h 0.181909489
1221 G06vCKjuln4m6u6CPQEn bu.h          NA
1222 iy9xO2TEZoxoxDybXwaa bu.h 0.648383547
1223 1dxR6XNJvb17STmiEGyG bu.h 0.107358559
1224 htQqEkZrN07nvsxITRGJ bu.h 0.305369487
1225 ydd1LumbkUH3nodzQ6ED bu.h 0.275850180
1226 BxdFCeWLf8nCNjrDcwKt bu.h 0.156850875
1227 KofgQTwOEWPBY5YSx1Dy bu.h 0.294783568
1228 CCxLeiDjlaFSE568xENK bu.h 0.095622187
1229 e8cXE9s9upZtFqszDShQ bu.h 0.369476927
1230 Mdmz1IMWbMN65ldqchyZ bu.h 0.340810549
1231 d7pCGcyjbWTZyaOtM2w1 bu.h 0.646191652
1232 hSH8uwdznJPF9W8CTA2w bu.h 0.172328800
1233 kgAJm1lblz7JIf8qQqZd bu.h 0.223584399
1234 eBDrjMp7gbOV17PYLZad bu.h 0.222645851
1235 WI3KBFZmJjw0Lxdzzcfs bu.h 0.187785815
1236 nSildUrpHNbPNfFS4sg6 bu.h 0.484748655
1237 tLdDkwsFunR4YoLKVhDr bu.h 1.029173383
1238 6CJJGkxU5iaX6fpHuyMw bu.h 0.715148953
1239 B0TYpKMbdruw8HtQz350 bu.h 0.584700295
1240 91aqlaHOAOSBKYzV8ZOW bu.h 0.262196139
1241 3Xnty4lyDFQ8oFoNZ8tt bu.h 0.202580117
1242 E8PGGeBqKQAEAtDPGAEZ bu.h 0.377882026
1243 tz0SRD6z9NGXGVGASUen bu.h 2.247044103
1244 OZxiKlHgf246UrOWZWYB bu.h 0.283599540
1245 BYqREX1vfi5jE9VoBNnT bu.h 0.865135205
1246 iqxjvzeGetsb8uS15L0w bu.h 0.171691123
1247 QisfbJl6cd0gMIAesKuP bu.h 0.241556228
1248 6ai9fIKVjoeI2vz5lgF5 bu.h 0.371306134
1249 SstzyshA6P3MsHarAJOC bu.h 0.141899178
1250 fVHHTZLFRLllmKkI5FbQ bu.h 0.240514397
1251 5akYQwXPqMXUdUwTlseA bu.h 1.007818592
1252 G47T4t0ounnVKcpS9K7W bu.h 0.261082126
1253 a8x1gY1lJ1R9tAnIoKt9 bu.h 0.222506001
1254 54siyd3BYjsh1mVk7R0Z bu.h 0.686239737
1255 KJ7i711vfHeh8Gc2wcSl bu.h 0.262320247
1256 fNDTW0uBz4kj8OONwszR bu.h 0.522554622
1257 jfLXVwgdGVIQFSfhg7Ml bu.h 0.019688664
1258 9nJsA4qES5mUIvG18pM1 bu.h 0.501875954
1259 hSA5CPhSl1WrEi1L9rPz bu.h 0.345213201
1260 zPdJNnMEELaOlKvgj0xS bu.h 0.125656034
1261 Rmo304xSZma4dcdYKeBh bu.h 0.097435597
1262 bKPckrWMdOayq0O1b1Oo bu.h 0.266333538
1263 dh2PexZUtPejLAUOwsKc bu.h 0.226554227
1264 sKCPMq72llUL2gHahkDQ bu.h 0.228548051
1265 Qic7eO9OFexru0SPkoaV bu.h 0.977578025
1266 iQPGv0PQCZ0WfPCRF2gI bu.h 0.542058640
1267 DwsLORNs9ADEuzI6TZqL bu.h 0.098281622
1268 WEx9IC5DSaAIytPAyJK5 bu.h 0.443476878
1269 08YMNb5ZqlRtva1JyiNb bu.h 0.217630341
1270 bXDeTn1VxCOIdVA5SdAb bu.h 0.135346004
1271 Whw399jpbWCFJWhoRlWx bu.h 0.044205543
1272 HQtaLSwh8MGTTpLVllOE bu.h 4.439501242
1273 Rw1m2pue8CmVp1Gbe9Aj bu.h 0.241557829
1274 k8HzSNRx0Np432Lb94rS bu.h 0.415177800
1275 Y82iEp7f2Fq0NtgP6rUI bu.h 0.324832879
1276 GlBRxPqUIjyWmzD1i5t5 bu.h 0.525983033
1277 Go5RkRHzG4ksr6WsU3PS bu.h 0.143761037
1278 GqmYmpAd9GBMU5ri3qRC bu.h 0.143373340
1279 tRFFPyx12L3p8DIjoKnB bu.h 0.297681207
1280 Ett3HfOGyyzoYlFcfvTx bu.h 0.183036445
1281 H1B2V5Xe5BW3Q6smlZl4 bu.h 0.408821372
1282 6fGC9DJqZxA4MXTQX8Sd bu.h 0.194841113
1283 AvPjrRe5e2ZgzIiYwRuN bu.h 0.183451702
1284 Sah225y6DfpWaipZUpoA bu.h 0.708900250
1285 EWGtEMZLxRLRxWmtzpso bu.h 0.937958815
1286 QygIO2O30zG9kOeA6yaW bu.h 0.228397855
1287 x9MGSL6YNBmOXrcLFokc bu.h 0.249414002
1288 IOGwmjH1oX4unT6cb6gy bu.h 0.191027632
1289 zdKF1SXibeBH26CpiufK bu.h 0.224101139
1290 iCXyy2xK8DDsgTtrf8kW bu.h 0.293107846
1291 OC5Ou874A2Om0SJSUBpC bu.h 0.209949597
1292 B4CiC8xT1jfHRpXt5XKT bu.h 0.146015368
1293 nzaOxCiki2hsjnziZvOR bu.h 0.316103199
1294 H1ZWXyVtnQuMt6kjEf1X bu.h 0.256113181
1295 mM99Ur7dro2BZKJ27iG8 bu.h 0.418620544
1296 We0PNV8gUFAgrSL0CLLr bu.h 0.321334387
1297 gIVXFcb4XIgyVfkGHGYP bu.h 0.145694399
1298 5rHhraHsUTAqh6sjU2d1 bu.h 0.277369294
1299 zNchUA3ho4s8iKcEP7I5 bu.h 1.034487133
1300 FuGtirjyvrzCpC7neCJH bu.h 0.543740690
1301 vUNlbVbzGuUdf0RQhjl2 bu.h 0.235043428
1302 IhFTvNKt25ewrMGGKF9D bu.h 0.251108241
1303 R0BAq19ThveXTuFrNFWR bu.h 0.859753788
1304 sWP0jhyFtpq9nHYbNzCF bu.h 0.227520889
1305 jRgId2AbQkaa6UHly2aN bu.h 0.210352708
1306 9kNKLFrAZTGO8pUedvTM bu.h 0.177371947
1307 jLHaeduCoUx3ftBfnPeK bu.h 0.126050436
1308 SuPzieu482Sf5FeELYmy bu.h 1.709768194
1309 c1osEz7Jb0wJHmy3939A bu.h 0.090338573
1310 Ct5lgNC0MclPUuSFZvTp bu.h 0.182405586
1311 MqZ2PnsrmMV01MfTVnxn bu.h 0.281445674
1312 ct6awrymUS8RgzTgYjKx bu.h 0.827585520
1313 cUamoVQNHYnryVfwEH5q bu.h 0.410430219
1314 BiyqZv07wmeZOboeTfnw bu.h 0.343039957
1315 wvOpOK4esubuC1J3qRK9 bu.h 0.205818388
1316 kWCRNYdWCscmc5F9HpUl bu.h 0.493683852
1317 OQBbRgSRcYyepR1DfiXP bu.h 0.293270054
1318 Bd0Fdaaq5nhrfRh9RNKp bu.h 1.238499789
1319 cCG6mnVTKeFUPwzMeFC3 bu.h 0.407488978
1320 3HNTHcsl7dZ2TGlwR4g6 bu.h 0.166037959
1321 n5AGPwfO2Wk5kjAAFQaR bu.h 0.274698142
1322 aknNulByQw7cFuH2L166 bu.h 0.407831528
1323 EJNTJ8WGX0f5L9UVArB2 bu.h 0.139622851
1324 ZusQXxCm3jbGtP0NYMhF bu.h 0.277592309
1325 WTOZDrMMdj1ghr0X9wTI bu.h 0.279619967
1326 Q7iLWceWwxJLQ6xVJGyO bu.h 0.417446157
1327 fjBANeVCQ9ucabPbYbtU bu.h 0.515473683
1328 TRGDCtTGdsfMNkeRZTNb bu.h 0.368508183
1329 KVdaeOj3Eyn3hnPNiffK bu.h 1.882306610
1330 IMkDozWulx7wu0qVyjW2 bu.h 0.452066403
1331 UixxkbQY5Kl386J292ei bu.h 0.131344301
1332 WB9EKGkTF3UsCmq9SJTj bu.h 0.417409123
1333 aBfvkSIlA7gXSkv4WG4S bu.h 0.218939877
1334 FVr9Qh8yXv6UmFFciuDC bu.h 0.090730339
1335 ySoH0vvW74RKfwvyBl6I bu.h 0.296172695
1336 mbE1EbeFe663nX7WgAek bu.h 0.304175860
1337 v8OOr4HHwaTU8MdlBiNe bu.h 0.840529601
1338 LGXgT64J11f1BZzL8rQf bu.h 1.137267305
1339 az533Se9msJWRtlthzno bu.h 0.226554227
1340 ma3tSscD5k0uqSh5XnvX bu.h 0.437588787
1341 eNXtdvWDFDTA7Xrc5UTI bu.h 0.448709895
1342 UTbnbzpfIOZKctb8UqP1 bu.h 0.352723207
1343 nDejJ2gTwPGj389BaBsD bu.h 0.091527697
1344 ZP9yL6PFVQSvSIC7bUee bu.h          NA
1345 EjsoVpqhWOiTqfeSVzaZ bu.h 0.225554094
1346 VuUcRpAtYNI7G5o2tlz9 bu.h 1.057094605
1347 R6U0YqqdHfgqkWkuCagF bu.h          NA
1348 gYlhCVSj5pa1VFbFdicj bu.h 0.485727811
1349 0x2cRlqKcXPG9u4HNEXL bu.h 0.236163541
1350 Sj53VnpD3hiHSfQ25VLN bu.h 0.575217831
1351 xcMkFsNfZNXsd3qP1DVF bu.h 0.086182235
1352 J5eWb6Pq8mCfi5w5m4yC bu.h 0.438492570
1353 zwOf6SigT5GZ65p2ohYu bu.h 0.845338675
1354 y1JEIOJQJeheqJoGWlQ9 bu.h 0.381672098
1355 J564sfvVKu6Jy0l0oC8i bu.h 0.226554227
1356 NfezsS2GPqZf3vUkvzDQ bu.h 0.097731947
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1362 lI4g85zoiUUhqmDuctZy bu.h 0.666124004
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1364 cbZLy4QLBgsjiiszHV4w bu.h 0.164608800
1365 ToeVsNlqNWNSypLVTqn4 bu.h 0.757867562
1366 YO9ali8wQZwJc3ucK9MY bu.h 0.363528111
1367 2t8eqhwiUfiSDnWfoHBW bu.h          NA
1368 catnqnqYkW0aj7DuBmRM bu.h 0.315112057
1369 fkOJ1NbnRk71F9aGqXWl bu.h          NA
1370 GWvg7NVGakvtiXu8z98w bu.h 0.662566079
1371 uDwAuOeTAbfEsISlrm7L bu.h 0.340892041
1372 O0NYuwahmBnGRb5IPZGa bu.h 0.226554227
1373 UemMp9G7JW85v2e3Dgbl bu.h 0.231855435
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1375 OFxLhD7SgOmQzJ2vnGcg bu.h 0.763483419
1376 ud0ctfBtDVNwiBGUTgWC bu.h 0.681614715
1377 Yfk4ShUhELUtl9XwpgCa bu.h 0.208775896
1378 QXS83CXvmp6zYnsLAuuG bu.h 0.138244131
1379 u705XCN00NDookJAZSuU bu.h 0.168430491
1380 OubtI4CkddppJogeh60a bu.h 0.198836792
1381 0c8FwHA6as6GWOBJRG9v bu.h 1.204632836
1382 zah1W6BFCRy5OdKRE3sk bu.h 0.374281288
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1384 V2ME98SReEaOQ3fhFVst bu.h          NA
1385 VS5l6y8k6nRvLQg16gSw bu.h 0.385765139
1386 GF9IyDN8pbWvTMVTucHY bu.h 0.652892394
1387 fkFoEo1gGQv4gTV9uSmY bu.h 0.154209978
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1390 Sb8i6ZwHgcgJg7H5fXHz bu.h 0.392363081
1391 PdFxJ834MDPUw4BmVEah bu.h 0.215835494
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1393 imxFcTXtwyNUY9uOjkct bu.h 0.166352621
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1395 Mtt1LXmZWBninK3ggSK5 bu.h 0.287694824
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1400 umV9t8yhNwHqLYO7llmI bu.h 0.706600463
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1406 cdIFWh4OfuE5crx6fZVB bu.h 0.077115079
1407 950AzNMugIUFBNRmlDCF bu.h          NA
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1410 omfSFxzu7MrjjSSafj2q bu.h 0.259563434
1411 TTdDRV6b7DxOANwKa3Hg bu.h 0.397611524
1412 kiTl14IWGtRXdVVJmZgD bu.h          NA
1413 4GjaGS0tN2RXrJjOn8AT bu.h 0.166791245
1414 ZDYUvh7i71S87XwXaHCP bu.h 0.804238507
1415 4MhlknTWNBsCpY9BSp8P bu.h 2.658825261
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1417 NH3PiPNoQ05mlw2FESiA bu.h 0.704826471
1418 3hT6jBPVahtgMDOshUCe bu.h 0.300826258
1419 ekGBkEpWxMdbx2WL17xl bu.h 0.278380948
1420 ADb2b1AvtBmftDmWdKWN bu.h          NA
1421 J97ljhO3gdtigy2V3olp bu.h 0.185855712
1422 oO4EVyNmcCaw95OzZ81f bu.h 0.320052747
1423 dK03wJ3XzhgE9DtBrUzC bu.h 0.408547501
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1428 JhW3ezpIB8sBZJ76QQ2b bu.h 1.327362568
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1430 s9BvYB9WoYKvSNkimmdv bu.h 0.282234762
1431 f4DYdisckkbmIZbW5fRN bu.h 0.176350979
1432 TKO2Alhu9oJQR8IzF5eI bu.h 0.226554227
1433 7M8VaQJav62AFRVdHM47 bu.h 0.172283581
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1435 AvX9Dhp3jgSThm1Kp5Tk bu.h 0.289230945
1436 F2DvteunZgYFdBB0EK9W bu.h 0.415610085
1437 Ewp3WFDEQVhQx6UMUAiN bu.h 0.428099523
1438 vbGZVDJU7u0ApalOtBTF bu.h          NA
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1440 dVSqZ84Qjo6hCYy4q7uJ bu.h 0.144625068
1441 TKkwOc2pHd9PObQzxHDj bu.h 2.903241333
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1451 MVHcDME931E94rnJdXDH bu.h 3.080070873
1452 5fPAvtKbs9M9BjcYmxU0 bu.h 0.571230008
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1471 I9oLuP4O3GOIbYravzP8 bu.h 0.174824094
1472 zKG5mSoyPstUeC99enq5 bu.h 0.375519798
1473 SJdYRJAFtFnPuPXeDtZ9 bu.h 1.130072594
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1475 s0XXgsxyI4HqXAssoIP2 bu.h 0.826097838
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1477 o50sSHWXHmN9BTohnmdp bu.h 0.495247851
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1483 887VJxVdyCaKr418yL1i bu.h 0.196697435
1484 REcum50sJgQSYvBMQXwc bu.h 0.381323901
1485 TuRmDIBkujdQMhZgsFZi bu.h 3.600746091
1486 QHpIC6N6d6A9qQCwV2nr bu.h 0.109643503
1487 o7A1QifxWSXLV6IS4ZED bu.h 0.206543550
1488 Z4ROUHLW7slQlkFxAIiK bu.h 0.121746324
1489 t46xHr4YxXBc3lF6k7Ha bu.h 0.765879364
1490 gYQyYFdGcutMxasXnyBC bu.h 0.428569198
1491 guvIeza8tdBEAbV8DHPM bu.h 0.332495991
1492 Je7hQucSMLQWqrCCwqTD bu.h 0.343587779
1493 egfzosHWsiOmw2b2M9NO bu.h 0.382068990
1494 zQq3VbrGpq27YcQkEJSJ bu.h 0.329110133
1495 zSzSixqybYEaHD7X0mbC bu.h 0.282295219
1496 TwhLb6p4zP55k4WSdyjS bu.h 0.844391112
1497 EECGDwmWAmWfpKKnYI7O bu.h          NA
1498 QMFZxe3WbmxMlJ9c7oxx bu.h 0.588891571
1499 Ayo5YHWlCQsHNV7XrQc7 bu.h 0.431234490
1500 t5l9pwBbPNnR4Rvr4RMl bu.h 0.489953700
1501 VdblB6kOjPuNZJSookux bu.h 0.619871045
1502 UIFwra15oVwoFsQMfzB2 bu.h 1.857380620
1503 XjY03SGFqyp7HmUABJbW bu.h          NA
1504 3RVHWOebZPPvYZwkVRaC bu.h 0.830564999
1505 p6gZwkCYLDi7vWOthkM3 bu.h 0.111802135
1506 zlPLQKWkCJ5fswxeAIsi bu.h 0.490894653
1507 vw9RHgQ3UaaTe1nfjJc0 bu.h          NA
1508 hiqkcqOk13LSwCaUedQQ bu.h 0.265608465
1509 IeElhKAlZlvb0pPCNyJQ bu.h          NA
1510 0FX5rBebJhUn0z8tQid9 bu.h 0.485007877
1511 0PDGJ5hkAxxmw4ziCQiz bu.h 0.281595245
1512 0ZYsHN4CfauJs75YWXmE bu.h 0.012213380
1513 YORDk6DFygvOTEfH2xnP bu.h 0.294488064
1514 OzRL0M3xJ0pxXrCLdZtI bu.h 0.288689723
1515 iMajxXV06Xq7yz9G7h9l bu.h 0.160955047
1516 haKH6sU4vzOFjnZmlX00 bu.h 0.466244121
1517 d5wOOcbj1lhttP6hbs7Q bu.h 2.431988666
1518 0800TgdtDlffSfpuNw07 bu.h 0.368508183
1519 tpeAVPcZPp4su9E70bYx bu.h 0.083027652
1520 crJ8yNv9k0znhIx2a7Nb bu.h 0.377288218
1521 QxndeQ6cTJXmLvcx0mSM bu.h 0.106642189
1522 9f0qLvOeqUpquuahieMQ bu.h 2.463150563
1523 gLW9BncdnIaH02q49IcY bu.h 0.083330620
1524 lb1WoSOHyGEIMuNgGeZi bu.h 0.543480910
1525 4zCxjdZQSFAMBokcJ98T bu.h 0.357503304
1526 brzhjpzsxAnxUy8IUmMX bu.h 0.118473430
1527 oORaDioQaRY9AiexTgt2 bu.h 0.182430375
1528 rhddayHF97oSYI7UrdRt bu.h 0.681961939
1529 rx776GuiHbM1PcQzqCMP bu.h 0.243525805
1530 LFkmqJfjmn1ZG04hIbCY bu.h 0.284687588
1531 h1VVrAsMOlTSjgsxu07s bu.h 0.294690966
1532 w8EG0W8jl0IbL9HsENCg bu.h 0.597412039
1533 nabsPHGZpvr6SgYzV2am bu.h 0.284513630
1534 TJns5NZcCMEwMuva3lvg bu.h 0.263744393
1535 3RXZTpxFAGsHEekF2MpS bu.h 2.060531364
1536 wBUnPDocOasWtOr37ldU bu.h 0.255660887
1537 2NmWZlOEpEqJHjLp7Y8a bu.h 0.251403903
1538 59CIsegSEuhOgM3tBMsK bu.h 0.203745398
1539 IinbrDyMN0ssVE4u2arl bu.h 0.316060602
1540 s8qlhnuyRiK83fEs6Fzz bu.h 0.243844196
1541 yrYjAH12nGQ4Nwmgr23I bu.h 0.796366485
1542 SmNqE40fAraVNqTIAae2 bu.h 0.442484405
1543 IIeCbY9dM0C04giDl66U bu.h 0.215937065
1544 BnhBrwCAP3y6BtcM44LY bu.h 0.314818149
1545 tN2DQnbEI0nG5Nu5ogz2 bu.h 0.272687236
1546 D8ziemVuGzthoise1ShM bu.h 0.226554227
1547 XMSgrGGYvUNWhsAfKKpC bu.h 0.360836304
1548 g00rVq4jB6nBvbdFo9xb bu.h 0.435565232
1549 9YuQkyei97xjOTwECX55 bu.h 0.441151533
1550 RQ8RuN2t0DuLKxNoK9oM bu.h 0.896932642
1551 gSZl9DZU0bcpGdNUzSVU bu.h 0.153244702
1552 C8dGZbXoGU39N6AtNBVB bu.h 0.222370112
1553 baWpZ45AscEjcjtTL7mF bu.h 0.362051761
1554 3EAX4lkBHl5nRtChoDyT bu.h 1.146805581
1555 w2Xldj8FBCgwduNf8v6b bu.h 0.110121896
1556 oqRF8ywLaUNfPwYgnXRk bu.h 0.435863620
1557 Q3ZWnoAF8Dfd8clLWhFj bu.h 0.373120207
1558 k7gyHneNVsbSK4LxbZUr bu.h 0.318343896
1559 SC2x1sOCmOGfCQ69ipBT bu.h 0.205516914
1560 G2076ZS0IEWQIAvbwCPT bu.h 0.205460821
1561 OyCfR97qmhV4y6AwIg2Z bu.h 0.346197662
1562 zBwDGnjea4EUavd39DGM bu.h 0.567978329
1563 G6LvMujrQyj0EAuVo6Uq bu.h 0.298461921
1564 7dG3V43eUrUZJTJAXX9i bu.h 0.464230736
1565 j0tx1EkGk3a6vtnrWWOw bu.h 0.200166170
1566 4CLdS8XZEmJVDlHImT7u bu.h 0.295695980
1567 GOQMVlwANYX2lSvvAvS4 bu.h 0.191068516
1568 sakpCgS4Q92eACEHYviZ bu.h 0.234515162
1569 44ehKBzJpTzvzvF97RXx bu.h 0.489102389
1570 XT0pYv8pBcC9qXmVHtyv bu.h 0.105197558
1571 gGaL9V5DuAw39Y6Pxdwu bu.h 0.202734548
1572 LMyRBPnNh3Elu6pQ5yHr bu.h 0.353451734
1573 C97KNSD7eM6nPKVQs2xx bu.h 0.288359344
1574 jaqAJUH2433eB4OodRSx bu.h 0.151316486
1575 Gp2PGuskkS8QiIdU2hkK bu.h 0.272592493
1576 LSSNI9CJdMEB7kIIdv1j bu.h 0.370938016
1577 yNm6Ltol5DXjwjz2vfYi bu.h 0.437679774
1578 paNIkLAolYQLDWiJ1rc1 bu.h 0.793523843
1579 QcrmHac8XMeZcwhfqCZW bu.h 0.186460554
1580 QYSywlxUihOc0S6p7nNh bu.h 0.582701715
1581 SEabyz9Vya7xSk90h7WN bu.h 0.395215087
1582 zbI7AUMspFhHJsUyyaY2 bu.h 0.128342648
1583 t6vbQHOtaa4IQAKXqEen bu.h 0.688388107
1584 naEx0junNZenxp3lgHld bu.h 0.217317645
1585 PE7n9OgQC7f5NlONaFnK bu.h 0.149830758
1586 M0AQNZG3Ztf3ASfDCjLk bu.h 0.370248684
1587 IBso49KowOEeRh12NApb bu.h 0.207314802
1588 qXLy4pIlDDDoNenqCw3e bu.h 0.386814101
1589 D56h2FihTO5mWdBfeZSr bu.h 2.798503052
1590 rkUT5E9X3gFoJaR5UIkT bu.h 0.363731890
1591 Y9lj2tOrXl1hytUGa6Eg bu.h 0.347485670
1592 1ju0WKtcjrT3fG7iuXNd bu.h 0.375731275
1593 P191ezqShWOHbDTsyYVw bu.h 0.993615556
1594 iyuZLCEoBQhUFziAKyiy bu.h 0.248545894
1595 cAYYkpIxMiubqvZJv80N bu.h 0.258797431
1596 HngYolsZ7LnwhMepx4yf bu.h          NA
1597 K8U8pBMuys6ldJBu2EyF bu.h 0.147648784
1598 s7mJzZjHp4ae1qDeD85E bu.h 0.698791347
1599 zfRwAzlz8YsnYmUk9PGb bu.h 0.434543749
1600 YadICsCbreoE8j0XqU5o bu.h 0.226413359
1601 ytBjfH91cVXBRI8fKxDi bu.h 0.148280193
1602 hOdVHTzPBsd26UMRFL3Q bu.h 0.242982907
1603 JrN194r0y34cNefXSrUy bu.h 0.126265706
1604 Q1tEh5Ao7Nirp0AUi0P2 bu.h 0.787695227
1605 Opi65eyMrbyYjB5bwRMP bu.h 0.347372149
1606 SvGbcZdLIcMlcSVZv8wA bu.h 0.292476032
1607 PPoF7VmcJfI5PN9okf0T bu.h 0.588604427
1608 vINjSqfdP97CnGjw7CAg bu.h 0.150867259
1609 BFJ82oQJcYVAZ2SMBPIY bu.h 0.243171118
1610 QsUNTZUvAPQxD34B6YFw bu.h 0.823923455
1611 AhirSY1w8dBzmuo2t48W bu.h 0.878511655
1612 cfRej3asg91cIUhItulY bu.h 0.178533781
1613 sBFTgOui0BzVpYvFfxUw bu.h 0.267582014
1614 qKSTkUdYD2WOZ5b95Iae bu.h 0.266469196
1615 S5dPr9tUNgO37d0wEWm4 bu.h 0.373556496
1616 gwfb91tqDWXbpOi8L2tf bu.h 0.160480072
1617 jWVRSGBb26oZsTvK62ck bu.h 0.148841693
1618 I6ht2TV2zzB535qWglVL bu.h 0.147191057
1619 l6LC2lOByjGR6Wz3wwPb bu.h 0.443814266
1620 HtYDZw69CrxG7YXipN2I bu.h 1.344090418
1621 mSclIp20483BS4ez5FbL bu.h 1.088650447
1622 JXlQoaM4jyh0d2zxigu3 bu.h 0.125393299
1623 HozmCUcCHVfHEc3uO79q bu.h 0.882556799
1624 h4BsMeQIsxfUoLOaxX2M bu.h 0.243893859
1625 YV20vaAnaPndIGbTNCyi bu.h 0.201680084
1626 gHWgHzynJbowDqymIDjy bu.h 0.516658522
1627 yWx2uJPTKZJGfAOeaat6 bu.h 0.280472142
1628 nIVIY5hc7DmG8frmbML7 bu.h 0.072092825
1629 vRdqlIejVhs4yz4OFb8E bu.h 0.226554227
1630 XVKSRHtW04rytiBbw9ZG bu.h 0.188529921
1631 kr9CWXPe1Jc4tybB5TDJ bu.h 0.079879879
1632 q3VsAGR3M7NcSy306kkB bu.h 0.391651598
1633 5njl1tI5Brmht6T3nyCC bu.h 0.708355360
1634 bdrKTO93ryBON0xFRB24 bu.h 0.368022610
1635 383KleAMN0hXjU1qXdYJ bu.h          NA
1636 l6WLNuf2HjJWGxOVFiYO bu.h 0.248314097
1637 a3DIbTUvj9aMDSKRRk9c bu.h 0.551337555
1638 yN6gTDSObped8SBRQTX4 bu.h 0.133431611
1639 vYHV9uj4nArPmhfjNM5A bu.h 0.341233172
1640 SQl7eL6Vi0ivHeBXD5fz bu.h 0.243793910
1641 i8uVNjD6U5P2koFh3qw1 bu.h 0.183557498
1642 l262n65OM48mRkyAqfw6 bu.h 0.715566580
1643 03spmysv917pRTSkTcj6 bu.h 0.235914090
1644 9LHZO7LhTTmXrXAYaqib bu.h 0.304431056
1645 r8rsLAWvh28zXii51d6C bu.h 1.076502738
1646 FqMdeNMqacNOMwDGxovJ bu.h 0.129030370
1647 to0VIfIQSnpmoACV86zs bu.h 0.478663531
1648 EVcWTJe85xOBSqi4fxyc bu.h 0.311517070
1649 3TUTJUD20hg4uydW0nPU bu.h 0.212188237
1650 5LOJDfEvGNpqqrFt44Jv bu.h 0.396064758
1651 L7pmg8joYv1DAbta4GSy bu.h 0.479064649
1652 eO2fxGLcdC5k0aGuROZx bu.h 0.399827257
1653 L431K0ZDgV4wqUR7XTeo bu.h 0.279369722
1654 AkV3y7cqvolp4mvYr2Ig bu.h 0.122683432
1655 gigv6VZBKKCRbQpR4H4f bu.h 0.210180660
1656 GVHAmxNyu6oYBUzo7J1j bu.h 0.351250320
1657 2hYKwrUAuifTR0RvADka bu.h 0.104375948
1658 V4NV0ap6EhknemOtzaEE bu.h 0.311677381
1659 A8SHBz2S8Ui095L35Wjh bu.h 0.724084609
1660 pabaPqShLcfFodkNxSi9 bu.h 0.196176766
1661 EtulJVHuPSc3ZLVKBPtb bu.h 0.287050577
1662 1h05DmzN29B6FZZR67pP bu.h 0.537892869
1663 qRl6I3vCdobimhTjNdFZ bu.h 0.593717710
1664 27zndIThTEYpFGYGVZuQ bu.h 0.095322716
1665 rBptcNpWoELaSQULtoMu bu.h 0.265078192
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1667 axvLHLzaK0Vc8h8JkYCN bu.h 0.111908316
1668 aXtAwUv3JWttnWXbi1wX bu.h 0.119527970
1669 8EecXzNhbAccCmHOBTFm bu.h 0.087348314
1670 3JAbXWzVHjOfIJvN5nIY bu.h 0.312204650
1671 wc8gFFDsjo1YttzTxjgf bu.h 0.389563341
1672 AjaMOfTft6JUA6zGOzJG bu.h 0.192339006
1673 sMqUVKK0y90g114RLcB3 bu.h 0.087087134
1674 8SvO5adegSRe2a8FwTg0 bu.h 0.360371063
1675 okwvnmmp0HLMbZU5kk4e bu.h 0.310798252
1676 kx22ur9KaLMALeSaRB69 bu.h 0.441620092
1677 CKbheN9Pot7GaZpfPYe5 bu.h          NA
1678 Q8xQjMfeUVXmPEqv1H7b bu.h 0.305211227
1679 L93UQXO4wGARzfPq08oO bu.h 0.739553375
1680 sp1prp6yBqCbHrTIgTfz bu.h 1.092387754
1681 9t9PlX7wfLMMjeei9kUa bu.h 0.145256247
1682 qSedMPNgMWvKnBuXotqt bu.h 0.092034952
1683 oEeXzWrAYrDZk4d3W3ZO bu.h 0.226554227
1684 CBjFu6W7QeUIwHec5mC1 bu.h 0.069251116
1685 MUe7xZKzTix8JJXQ0oPT bu.h 0.967902025
1686 adH08twQXGLnyHCi6ZEC bu.h 0.769370051
1687 nlwShoYSDDnjZbcJjNcJ bu.h 3.213682065
1688 ViAOJ5c4xcmlicXktJDv bu.h 0.481927145
1689 s2iFKXksYCI6HlXvQbOr bu.h 0.448420912
1690 4HVHRJ9CikK2DcK4oXP0 bu.h 0.257236360
1691 zMXD254LotKBsOco4pqG bu.h 0.102028299
1692 Y4wLZITrk3Kum0lcuesK bu.h 0.090225652
1693 BpXGyUgvqzapjyMB15Zy bu.h          NA
1694 BnD3XkfFNuYUPnmbpD0e bu.h 0.360919090
1695 klZuuzk47ZvpyRkPJaZf bu.h 0.466736364
1696 cLXoSFiMo02a11WTFSyD bu.h 0.267018926
1697 BTnpcne9levPSXUihg9C bu.h 0.215743826
1698 Jf8DG5xMJCoC8Kyclmls bu.h 0.138591111
1699 CNX9AP2rnXxONuGYEalT bu.h 0.434295955
1700 AkMUTqRpWfGahOvzrg71 bu.h 0.490806493
1701 LERGCSeJS48QJZJl5PmN bu.h 0.419968062
1702 HFmXofKOwruJkX7ka6yA bu.h 1.683964081
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1705 NDVCwpxotUAkjHRC5xdE bu.h 0.296426237
1706 l5k9jSqbnGbzXCMrdGhB bu.h 0.125636999
1707 hihTqB8ELx95P6OtHxLK bu.h 0.208575172
1708 DoFCE00qFvG0xBr3UWpI bu.h          NA
1709 64RVc86LLeI6qccFOAs0 bu.h 0.242387538
1710 hgZ7ff2tPIwqFV7t8lq0 bu.h 1.161317736
1711 Bipzf7LJ6qdGqAscZzBg bu.h 0.268586299
1712 JppTQOpQCcLIflZd3Gdo bu.h 0.459418807
1713 G1HTrSNlmhPaxLSlRHiT bu.h 0.549051950
1714 2sNFytLCdDNZk8UnzLGD bu.h 0.913314564
1715 kFgvXkoIlIrHD1GwLKbq bu.h 0.022826946
1716 zANbOJqdtyEpAq00umSb bu.h          NA
1717 8JHUdKF0j7elKPoh3pKM bu.h 0.522205583
1718 s9sWUlKi61LvdtfMTgzg bu.h 0.604982450
1719 BQp04wU2itT03Zya5nw8 bu.h 0.213519470
1720 boffKPQBu09LdqOpXQOe bu.h 0.518020674
1721 wFaqLYEi8CtquigOCMqs bu.h 0.222803640
1722 TP6Rekgd2IbyVfqjO2tp bu.h 0.284128010
1723 QUXPoKjCFO2TXJAKFj2Q bu.h 0.239134456
1724 KTfFRDs4otel3iaDulqe bu.h 0.317051468
1725 Czd635sdCxOALvFD51Dv bu.h 1.461824612
1726 NPDs44Y57UUyhFWDsck1 bu.h 0.611510820
1727 jw8r3A4Xq0OQdENkOYbG bu.h 0.727269877
1728 Ktd2aatpEAfmRXFdSH7f bu.h 0.226554227
1729 H8Ae5XiC8bzVLOueoI1d bu.h 0.209548756
1730 cF4Hy6MQbBHjtLu8xQRm bu.h 0.450002987
1731 SRQalRE41tZQMA4ede2G bu.h 0.535530635
1732 pHDREaIfWQRfSoDuPjmq bu.h 0.578840546
1733 Qe1hbaEgCL8s4t6r0K9h bu.h 0.286600311
1734 d5e2sIFpDIyXNBSWUNl5 bu.h 0.123035049
1735 mcxXOYXKL7wCD8w6U54N bu.h 0.069636340
1736 HcgfdvyC1AuNCwK2XnM1 bu.h 0.062171132
1737 N6wowD1y5KF7dmrevwH4 bu.h 1.688958670
1738 1xNiXXWkGtLI3YPU2dah bu.h 0.134004823
1739 QKfAH4AsGeJ4vOFeImNb bu.h 0.628752041
1740 JlC8vtBxKJRSYnZxy3cS bu.h 0.385650554
1741 BdgTQ1C8QkSCuLiWUM6G bu.h 0.367081480
1742 30F4d4EcTkkPxFbHeBiN bu.h 0.203904441
1743 2heUGNZnvLjewCSzcWx3 bu.h 0.312798284
1744 Tw3fll1lETObklyLeep4 bu.h 0.127052264
1745 H7f3VT97eqxqR8k4gLwC bu.h 0.156004712
1746 vSgAkbmZBhLr5kGKihkG bu.h 0.225686035
1747 fmSLXlMJjBkZV4HFV2wM bu.h 0.271348214
1748 aagK9U6xv7nU60Gpn14D bu.h 0.470797032
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1750 THM75FY0853siUB2wF5J bu.h 0.380935035
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1755 XDOPO1uoLt19UpVvxQMU bu.h 0.437774820
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1764 Ij5If0n4tBMsNtacFvQq bu.h 0.373391388
1765 rwXh4OtqsjeCqJsJDrVf bu.h 0.337205807
1766 GFZDz69YiwKEpC8nh3Kc bu.h 1.519484560
1767 s16ztfeT6kdUIE27AFTD bu.h 0.176521259
1768 EpT8wUm7N72Q3B7GqQ3y bu.h 0.208687551
1769 k9MmGT1uWvO13aGxHsEY bu.h 0.281242115
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1771 LBg0xU9XZnPijXtSAo67 bu.h 0.447816401
1772 4FSjZtJEUXGQaNQXwR2P bu.h 0.736278448
1773 c4Ybnm72fa2nYRZtcTiw bu.h 0.219477105
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1777 NbrnTP3fAbnFbmOHnKYa bu.h 0.240107184
1778 EJEY58XUQs9PBiy63T8T bu.h 0.197650986
1779 eY5DtwY3LVvSbYn1iohi bu.h 0.879201825
1780 Owa2LoPD0gHy7KjtL0db bu.h 0.191774295
1781 GsAXAPPN4fDIcj4bozSS bu.h 0.304936576
1782 ItE5RZJzDMaydeYzryZK bu.h 0.267565105
1783 D3ShceKj3iyKrXmCrB7j bu.h 0.364644388
1784 YILV2dj89tBcFUlQazxE bu.h 0.083552408
1785 m2HYtR7KxdR1TQFUGcgN bu.h 0.276603466
1786 djnf7XA2hqEFdcudafsh bu.h 0.172135212
1787 KksnW2TeKkjlvUHvX5zU bu.h 0.307755199
1788 zYr2MIonXqQnDBkZokiz bu.h 0.967874430
1789 hmmN8bnXvAkt9IUI7YH1 bu.h 0.467820919
1790 lWmRKxaFqAUiFJGMoQ5r bu.h 0.246928269
1791 lky9gKMGZreLqNRfLQb0 bu.h 0.367910097
1792 RK3vEJLww3uOQxINBsw3 bu.h 1.692128524
1793 iDVSD6gbQgM8j3P2K9pE bu.h 0.979255126
1794 5KVDGaVVAHsiHJMjLWGH bu.h 0.616191723
1795 f9ZLg4zeFKFULIdbmykj bu.h 0.874979508
1796 o24iqAR0WC7cR2ZkNHie bu.h 0.201588185
1797 NcZhGnniHSPaYFta1kv0 bu.h 0.154748818
1798 A88Jsbo66uN924qfUcBz bu.h 0.490585067
1799 bUlYPKyttuvpKUcRBNW9 bu.h 0.239974511
1800 dSPhj4a8OeOcH3nwDPnU bu.h 0.474758634
1801 cGDXYCoIaT6isOfDsR1U bu.h 0.993204966
1802 yqMj1TFPsIaB3OebJIsk bu.h 0.403908941
1803 R6M8a2s8Oin7lhCtFEhQ bu.h 0.073411190
1804 OqVMCBzCFg8z0NHsOlP9 bu.h 0.229308396
1805 nNv1U7RDvAlO10CNVduv bu.h 0.232320949
1806 67wzWEdx3Wws6nuexAIH bu.h 0.064767143
1807 IwHPhGIaRaumHHyGDjS0 bu.h 0.173771250
1808 CZuw9O4lHO9lFgor3kFU bu.h 0.121120326
1809 AMj1OsVFQi7SVE2T2GZ1 bu.h 0.258238344
1810 LvCQbOAkPqt0QrjrEbIu bu.h 1.012520541
1811 HNdc06PVz0oRaFxMPMDE bu.h 0.155712755
1812 9PhfXbX5j14I39Q4Vi0X bu.h 0.323373275
1813 RveeQdZHVYmtzRhFqxdE bu.h 0.209937472
1814 Gm5YDXVt591FIqCgxMtr bu.h 0.078907301
1815 QJAVFnxIfKtUQDRfIcd9 bu.h 0.415582185
1816 J25yHhTCXXEKWYI31OxK bu.h 0.541313817
1817 IJumhtN8oKoYEDbFfUMf bu.h          NA
1818 zwsDBXQLvSb1N8KuyQmf bu.h 0.191440861
1819 TzFZOXZnhIQE8OWTcMXv bu.h 0.229245667
1820 48JkxisEdAAEeppMKmgs bu.h 0.457133423
1821 u2ImtLqPCHaQdlpUDDTH bu.h 0.252569717
1822 Nyrg5OD09K6qxEUfEKew bu.h 0.142188165
1823 ASNPnjJhR3uNgopLHQ9Y bu.h 0.274606407
1824 sZGzqAOV3iVsQwHtXKkS bu.h 0.265598791
1825 nByyDx5ySIqzXH4X85Gh bu.h 0.165482505
1826 9pA1UhzRyENKrwU6UsUQ bu.h 0.452307776
1827 ftg3eOdbuxVc1zJ7ZYS2 bu.h 0.621781165
1828 bluXweOxWhtauCX3IxLG bu.h 0.226554227
1829 3Wk3c1HGlTGUJ1UbAUNw bu.h 0.217490239
1830 3tTUMdxsjmc5L6TurNVG bu.h 0.743290916
1831 Ckaj2seomfpQ0xDvDQR4 bu.h 0.227616778
1832 tRx9JmpBrmkYYlKNPmY3 bu.h 0.118203069
1833 I6zwRZqces67gSGlGpMl bu.h 0.117338471
1834 d89URw7prNWQ2tYiyNE6 bu.h 0.704022237
1835 iqav0EdhDGMWKHeygaoS bu.h 0.616093184
1836 f8xZZy3AncQYYmK2eysC bu.h 0.195998652
1837 TzlPfTntMIE3CN8rUEgB bu.h 0.234807587
1838 JZiTwihTC11GKlKM07IA bu.h 0.437637387
1839 j5bxfVlajJwn5pZNQQsa bu.h          NA
1840 4MIJuy5zAsuq35Utpe3L bu.h 0.148139063
1841 GNtvHXBwqsaY0vDa56Da bu.h 3.479578228
1842 JoqyNAbPHNMUKRe5gWsf bu.h 0.697953938
1843 6jOSL0IZbcNJOVzNEMr7 bu.h 0.223090420
1844 cw1XsyeMghuuUJiSV9Qh bu.h 0.264117452
1845 nEtoalBY0FuCldIuVyG8 bu.h 0.172123101
1846 TqSddjjditUnblQcWfB3 bu.h          NA
1847 VbKQhZ1CR9KP0NGB3TuK bu.h 0.437649494
1848 8AcDHk4eXDDaZAtdlBsu bu.h 0.238555827
1849 LT2PWfa1d0t3mjn0w4KZ bu.h 0.563557560
1850 nvEdRy29lvIepo6irKSa bu.h 0.523489429
1851 GeqLFnYuyrcwpuJJBlxp bu.h 0.688139061
1852 VLLHim4Q2j7wFMmMQbHq bu.h 0.760644360
1853 kgP1UJdbpmTSUhJNiSt3 bu.h 0.838481010
1854 6rPerw3fPtggey7LPj6X bu.h 0.421418068
1855 EjoIAEzk6UUhC7y9F0Ry bu.h 0.088197614
1856 Hy5g7Y3sJf9BcHPqEZ8p bu.h          NA
1857 Z8vYqlN7AZH1XOE9fVW9 bu.h 0.552298962
1858 Xf5wK1xsRt5nKLDYQwop bu.h 0.349217050
1859 z9tcGvLAC8Xr8EQCj7MP bu.h 0.070409685
1860 vNUhkysUUHxRDi1a5BQ7 bu.h 0.682954112
1861 oaj9WvyKGTefrZ4v7rLm bu.h 0.264816875
1862 PYH1CXUkU4mAxBsW5UHi bu.h 0.212363360
1863 m2g6jSBVMT8QMYfBVVps bu.h 0.319243530
1864 lGg2blsvC7fxdUOfsJJl bu.h 0.221664869
1865 p7jxXch3zyEvD9B92JRQ bu.h 0.226554227
1866 iNTbsxT1Zr20ZU3Jqrah bu.h 0.990347763
1867 Fh8Etkgq6MyfQfZKnifu bu.h 0.448018512
1868 mz5gJk7Bysn1ctNZy9nb bu.h 1.540072117
1869 OwOc8QyU7AaqFGJJByVK bu.h 0.186474729
1870 ETLoAZ2H20kQtboU7bWK bu.h 0.260516963
1871 eelRfx1LY8wXJoRoNwGT bu.h 0.256559830
1872 UzgNwCP2klyuj9Bvt9ux bu.h 0.227452689
1873 0i0wZSbTdy53z6Cni0Dl bu.h 0.267435384
1874 jwrZAchUJllqwC4lOtJw bu.h 0.262165070
1875 nRnUY9CTiB7LVJe7haxE bu.h 0.188229823
1876 JLnGgJO2Ms84Cyk7fRai bu.h 0.381881594
1877 ekoZy3uUi9SF8dBZvV7y bu.h 0.139375229
1878 2QJZGGcIQF8A5FhBmyuz bu.h          NA
1879 M137NRxqwFCoVKWJPFGt bu.h 1.349237025
1880 AIreHXDAPHzQ5NBl7h1s bu.h 0.401492087
1881 jELaj4QGeubKzHkFpLnO bu.h 0.415197996
1882 d1jMVfIndO3aguyDWGQR bu.h 0.171695992
1883 1UyTuCcPk3kU0qlGCyiZ bu.h 0.431946233
1884 T9VACiwBbmZCwXUD9UDS bu.h 0.743570859
1885 xNOsvtwr1OuWH1xc2kyT bu.h 0.226554227
1886 jQzW52TdimuPGtk4vvVS bu.h 0.578309602
1887 jNBKatcwaUXY3CsYCnbg bu.h 0.275108177
1888 OJ5jre57VnjtURyBCSjz bu.h          NA
1889 mXMCaULEkFF6s1prItB8 bu.h          NA
1890 azeC5Kk3x946Oj4L6HnU bu.h 0.144996578
1891 9fNzCWLAlFIBwlLJ0oFz bu.h 0.591049004
1892 AsoaxzIykTyxqAoV4YQr bu.h 0.201697804
1893 pg3CssQWWIcR6REE3TFo bu.h 0.355148245
1894 hWubz3CZRTSYl0pSvT6u bu.h 0.646953618
1895 771cxdbg1Rw25ZLqI3Ge bu.h          NA
1896 xSvzfB9wIjnw2QuEb9Ba bu.h 1.221784626
1897 icCyxLN7FqcdefZiyNnE bu.h 0.271867592
1898 L8IHrPObgmoQJmMQBaxx bu.h 0.665960054
1899 X0oLGEuROMMZmJgGwq1Y bu.h 0.477389153
1900 bRtADGpq7hsZ7dqIDhah bu.h 0.464074312
1901 wef3OmvsUJkJdlJMTKTo bu.h 0.214767870
1902 S3ozFIIRmmWlk0ykAaHv bu.h 0.094089975
1903 0tveH30S5NXFhmiXbIwX bu.h 0.274428777
1904 WAWHoPe5JEKCIYk7egYV bu.h 0.152390285
1905 OmDvk4v7NxuhAnLBrkbW bu.h 0.242640301
1906 KDzeLpHMIZdjOc0PZs5d bu.h 0.248803269
1907 kpyj8IFrc4680DypKKjT bu.h 0.115992884
1908 lNimfKZ5oeStSpkprBNq bu.h 0.286127343
1909 m5GxKzrhPxqHAF9y2GDA bu.h 0.199458526
1910 ZXe1cb51JJRzhbuXMZ5f bu.h 0.193824643
1911 NsnSnPBoVOM0FBnTLBUg bu.h 0.557698857
1912 3v8cpIfuZbk4rsihgtbl bu.h 0.569312912
1913 RWExy4eQFLJmpMRMH880 bu.h 1.529762104
1914 Llp7QT0WZn6H3JgzqQ4l bu.h 1.585384226
1915 LmdItKKcaTPAwe0Ym4Od bu.h 0.132491678
1916 qexF3lX87eCr0uIamIsM bu.h 0.312460531
1917 ZxO9B6Umeu90CwG7xIA5 bu.h 0.559820614
1918 49bWZY1TC7zcMYq4FzkX bu.h          NA
1919 3bYsrERg1i8BZqzOlnZT bu.h 0.837660421
1920 iMIXS6YZaNz1hJe1rfHh bu.h 0.121415306
1921 fs6yB1ktplPsr5geI779 bu.h 0.165819056
1922 lsl2rntVeEnvWXFypJ9k bu.h 0.300154677
1923 3VuBzxhMEP0vh3PsqIAC bu.h 0.667274941
1924 bQQyK6ZjfZOC7Ka8O6uT bu.h 0.290302546
1925 4LPIECoxgavG7Cw53W86 bu.h 0.233036269
1926 BdqfJfhiIt3nRvRm61xp bu.h 0.239555961
1927 2pScWifucpmQZvXoZCw3 bu.h 0.464251808
1928 LM4rmItUzAPwedEbj0aj bu.h 0.783573388
1929 FQCQhjkNqqJrCXXHGWyM bu.h 0.367268393
1930 qLd8F9TWaTwM4TqGeHyu bu.h 0.296905707
1931 fzuvw9IMx5kMEU5m0lQu bu.h 0.101777428
1932 YwWY4wrREDr4JqKcAYda bu.h 0.286267858
1933 kf4aCVgEELoUEYO0Sl6k bu.h 0.567646290
1934 4rY5i0hQ8qKc3mBAnAwT bu.h          NA
1935 T1GXVTI6JtKUqYT7oBIK bu.h 0.207536448
1936 7oqtXRTtqemWKjkCzH7m bu.h 0.351303044
1937 ItLyRC07hxMbcaguQBkl bu.h          NA
1938 CFx2CdZ1bSxA6JPb6SA6 bu.h 0.373674067
1939 YPnkwtG0Yh9ThBhomIz3 bu.h 0.099570423
1940 dql7LIbSPrfB91kZLXYl bu.h 0.042348446
1941 UhiLfhGnF49zBXe6mm0V bu.h 0.114322505
1942 Mi6oc81FIZdzpX92BWed bu.h 0.819352630
1943 4oaDoopwxTfQsZjfqWCG bu.h 0.292529910
1944 A7wLmahMacmd6RniSE0h bu.h          NA
1945 zsYRR1FDyTt9ubPsiZRd bu.h 0.268112257
1946 8bdVYH1QTIdVRAAiB38w bu.h 0.270198490
1947 ZxkmcYeqzATKhbYKzU4L bu.h 0.386223590
1948 4MXqlRYjANDHSM003I2c bu.h 0.702343723
1949 QdUrtE4V9YCVu4rhYmEd bu.h 0.370529644
1950 vJRJChfTlK7VWnxfSpZN bu.h 0.255540901
1951 h4VGMf94mmMvjWkSLlT1 bu.h 0.278150918
1952 gxEhnJWw4xLuKjlQx2Ys bu.h 0.226554227
1953 i1mClzOboBJFQXyYZcX9 bu.h 0.655190347
1954 McZsAbJxyBZkxN6R06tL bu.h 0.098457436
1955 TCiP5bUtVehkyQwEQ3r6 bu.h 0.407144122
1956 t5fbyLOBsaQdFiNQyUea bu.h 0.106515923
1957 7O6mku0Wul8BBONaop2n bu.h 0.517717497
1958 E3hK4Etw5ylsn1SsXH8S bu.h 0.783743324
1959 GoEd9Sf53GRx8ffiYeJA bu.h 0.444984070
1960 T2kSSE2Q1XBgo05rbn7y bu.h 0.183984755
1961 cU37HwPJT7ClWoOdeIB0 bu.h          NA
1962 YHFbRs7SQ4hFsRbfcG8d bu.h 0.195821977
1963 i19KaWNFkrgchXO6nMfE bu.h 0.408379284
1964 LET8WoVedvwZfxVlSeK9 bu.h 0.139403188
1965 tqhjQZRcZufpwFPq9bzC bu.h 0.334163901
1966 muijl8y9nn0y7pk57na4 bu.h 1.697153921
1967 lvf066vjFToJgJanlr3H bu.h 0.129956321
1968 aPqjW2VygMcNCHKuJegI bu.h 0.605615241
1969 GPD6F9DZ5KZ649zqEkmE bu.h 0.206982097
1970 cthqlIctvxHks5zEjCta bu.h 0.120117052
1971 imzJ4CtSWENkNaqVkVEV bu.h 0.114454281
1972 0WgPYP5L2MNMyFtsWEaG bu.h          NA
1973 HBIYV7QNVglHl4RyuXzQ bu.h 0.319877612
1974 Cc2Gwx0VMe4utehSMqHN bu.h 0.314756487
1975 abY8uFHKTekjWnOLXuQw bu.h 0.338274525
1976 OnWF9xAaNJRvOuX00KEc bu.h 0.226554227
1977 hIGaMq1l6E5FxVGdqjeR bu.h 1.388895561
1978 cl9Gmy2f2bjCtUgkn3wY bu.h 2.361707951
1979 6k3jgmlRSTqrodJ0jwAs bu.h 0.399764584
1980 SwYoV379LawGLD0kqFcx bu.h 0.034085615
1981 tGYkNs8rZ3ChTvfNMKgS bu.h 0.511580989
1982 V3lv7Tbkn9gp6fZ5oBCk bu.h 0.243398679
1983 EYYpsISJ9Gn6xVcClZge bu.h 0.484550282
1984 4ErLxgNrw5giUuWzjjsm bu.h 0.150748020
1985 mSyezlXPEYufS2qNT60f bu.h 0.167818297
1986 3b1EQbis1bUOJqnwnf2N bu.h 0.302375583
1987 mk2SvIlb04bQxWg6soYO bu.h 0.148227923
1988 5eqqhP6CMMz3wtzLhe9H bu.h 0.467884038
1989 ePV1AgHR8WO6UjCufdXI bu.h 0.232736043
1990 scAXCg4KbFpiAMozPf8e bu.h 0.437976119
1991 NS80TKIsEEKLQYESGAtd bu.h 0.740521453
1992 Xv00kKXR4bR6IIl92EtE bu.h 0.181703703
1993 Y7gbtP7ySeQMgV0Lh8Wv bu.h 0.296667749
1994 mBdoKoU3XJ9aSYehlcvO bu.h 0.658186221
1995 gd651YaRgBbmHmGqDTiR bu.h 0.629968456
1996 SAQ9CRy6ozWjXwaHf0lN bu.h 0.472781405
1997 UXHtOtRF19fgRxUkUkB8 bu.h 0.141093167
1998 dDr1YyBAyPfLjg9qDv1Y bu.h 0.197253117
1999 8WuPfBWxjVA3Df64j8Yh bu.h 0.323436035
2000 R81zDdBsjgGbqyqUWMQT bu.h 0.412923218
2001 dFtCdZe11MFCIX3BYOtD bu.h 0.276588167
2002 nI96xlUaBGatfwp5LcrD bu.h 0.477149248
2003 RsRxUuQovzhFUap8a01P bu.h 0.373876511
2004 qkYeT5dGoVDovKxUcI8N bu.h 0.344769453
2005 giDo1r2zlRWJhaOQtwjO bu.h 0.268595445
2006 Cxv3OL9bAloZXz0mUogg bu.h          NA
2007 fAPx69vDlA8qSfqNFaag bu.h 0.255780339
2008 0GHoG2xHG1S8ObKb5WSx bu.h 0.218042753
2009 tMR3y87RWvjTV5kdfi2h bu.h 0.141430376
2010 CbhOliKxxKcRN1aAuOg4 bu.h 0.223734806
2011 xEOfY34NajjGyJmQtJ4s bu.h 0.305870035
2012 5FK6Vbwq2sUGNtVEgaFU bu.h          NA
2013 ycyAacT3fNqaHh1KK9dT bu.h 0.433876745
2014 RiL2bfbAKYkgUCWcsplG bu.h 0.350494703
2015 zJKMPzbHwSm9ZOwdtUPi bu.h 0.653429034
2016 gHHwobImT2PGSNn0oJPf bu.h 0.456247537
2017 Rq5U9Lz0pF3xtvZkt1Ux bu.h 0.099624175
2018 g7rIRsOq2kkH8pYaz7fR bu.h 0.267502469
2019 icpRmmer8RMDjezeZ9hd bu.h 0.112394685
2020 G2WGBZc9GyTIFNdsysZC bu.h 0.115069833
2021 rVcV4RQCRFG8K2N3i8g5 bu.h 0.236476677
2022 3lUidRlitol2ksRqEJvN bu.h 0.206975987
2023 6Q3RthfVul7PWNlhche0 bu.h 0.206139507
2024 SqhyFLTUrs6u8geASTKe bu.h 0.394041919
2025 WMJVDlVuUL494fab0Do2 bu.h 0.742845159
2026 pYXv5jekuYIsCKNfEukj bu.h 0.217105989
2027 oF9hBAnlrGYSa56Xj8G1 bu.h 0.982169375
2028 bW7vftpeg6L47YzZjzWX bu.h 0.301214713
2029 O88gcs0iBczTV5V6mLTh bu.h 0.135773735
2030 CtPuhMjKdihqEsXucpww bu.h 0.164768388
2031 rHoyDZKUzkS6LFeBuzf5 bu.h 0.241824016
2032 pShsXtwszn45B5D0Sf3a bu.h 0.433182754
2033 koseFfJYBpIeF1CO7X2W bu.h 3.072698948
2034 3t2nEqai6BrZR8VAubQP bu.h 0.156671603
2035 QFPR5BC0ggLW6ZcgN14j bu.h 0.119016972
2036 amrWtFuaQvSMbkuuP4XO bu.h 0.124055554
2037 QLyEuiyGskZKXWMVQm4M bu.h 0.167689208
2038 4yvivHyWLfFpSdW1qGZu bu.h 0.562567996
2039 2iVCvt2uDG7ilHZmkqP2 bu.h 0.271323720
2040 GEr3u7E6gNBSrk80PyiD bu.h 0.910933842
2041 fCMnEWhTh7kuGhOSMhTC bu.h          NA
2042 qpQ1vSLveBFouxZzN2iu bu.h 0.647058031
2043 1bohrO3vq1ZQNnSrphM4 bu.h 0.075198399
2044 WZGI7tv6OOdwYyw1ikSg bu.h 0.226554227
2045 3WOzLROmRBgFy8GAfJZx bu.h 1.909379031
2046 c4Vl8k5Mz917NgkI3qRH bu.h 0.518010970
2047 UpCXiaZ8iZxNNKqYbd3B bu.h 0.389434743
2048 HEX7q9dj3YP6yeKjY1io bu.h 0.471069156
2049 D44hdrnbGBHB5xj2Gpho bu.h 0.122043909
2050 2Vwn0iIcQkFTqO8avFu2 bu.h 0.139185718
2051 7qLDDKDuB8YbLuP5HMQ3 bu.h 1.152279324
2052 Z7HAk5IK2pAcOuvwDVKU bu.h 0.421559480
2053 D0FyE8Mv8tbcZvUb3vgv bu.h 0.454968530
2054 XvkcyeADdMidPbdn0tTb bu.h 0.483308107
2055 a5F2j7cvabLVMfvG55nK bu.h 0.658972232
2056 5llz14NWMVCOAnVPYcDm bu.h 0.116357441
2057 7vYEstY8KitU7rruFhbI bu.h 0.373239807
2058 m7jRKz68K57DR1eMddXA bu.h 0.462896042
2059 MskJPTzjpASwSGntWf2Y bu.h 0.129167492
2060 iOXLQN4KD6oWnwM1cDAY bu.h 0.252872591
2061 MuZ5X7AI8nx8OR1zFIWw bu.h 1.248593994
2062 aK83874g3oMsLQIwgpqz bu.h 0.205261755
2063 phlhHVlnES8GrgmolaHr bu.h 1.378280750
2064 h4uUfeR4zbcyISP04L14 bu.h 0.276403870
2065 E8yaAfmQWNuct8Fa63ay bu.h 0.232688719
2066 ycot7b1u8qAbXVh1r4z4 bu.h 0.737953988
2067 0BfdNKn6rbHBPfFmtL64 bu.h 0.083974039
2068 phTExIqqB9r4Eh0C3BfQ bu.h 0.218977414
2069 DRrgI3jzHMjID8Q48PjB bu.h 1.451992055
2070 RVDXEzosF3WdVqtyBy4q bu.h 0.091165557
2071 wTGPje1S1u8mlwHKABjw bu.h 0.295435175
2072 MyHTs9661HDppd5Y8FQp bu.h 0.953401927
2073 pBvR3Z65nOnnsikwV4gc bu.h 0.793653981
2074 Y2lFlepXHqWUFbEKCNwq bu.h 0.449524954
2075 K6AkkvoxJfeZWlPZXWsr bu.h 0.217825946
2076 HkOwOZGx4Gv1EDLFikeN bu.h 0.276585760
2077 Hnfw3a5JlZoOX5ra3zUD bu.h 0.328412564
2078 97lEJzdzHXPBSr47uRP0 bu.h 0.110632697
2079 r1YXkAsA122wXE9tv74E bu.h 0.260076582
2080 P0st9aEQhaLhgmDZBdhz bu.h 0.565592587
2081 DRqr8hSzaMnwXidtehGV bu.h 0.318260082
2082 kEsM4qtWu3c24cxLGja3 bu.h          NA
2083 O0yVeeQVT2OJE6mbt5wl bu.h 1.037417895
2084 j1lSAtVBMhimMpC1Ha2o bu.h 0.212284636
2085 ur0bltcfUPDHCL7htFAq bu.h 0.148905654
2086 Fvv0ALwZPTKLL7OVVBmn bu.h 0.426163233
2087 rC8vlv2BY9Kml9ASUSKt bu.h 0.328258139
2088 UYz0lrNMqMlbdHlgqSTo bu.h 0.347070888
2089 x4XJ7SUOoiY9RKQhe1Ig bu.h 0.365829103
2090 1PZ7AN1rSrpFAZ8fgXpE bu.h 0.166536837
2091 osEXywDfg2Ur8uPhCthR bu.h 0.316006662
2092 BQNfEfAvBdhpepuBqR8T bu.h 0.274553869
2093 uKYfZr65pq4KTBvCj3dE bu.h 0.117806889
2094 wTvMiG16cCRbflcLBehg bu.h 0.311326293
2095 xDJ0JpfeWhke1GvUwHem bu.h 0.425781390
2096 MueU6tcuMKiY4RzoX67n bu.h 0.115025295
2097 0vQoOOqbZMP3otq0XdM9 bu.h 0.636784663
2098 BlbFlr57Zxe7vC1FZ2xu bu.h 0.350581361
2099 j49ae9H3AT6mRC6ygqv8 bu.h 0.957233014
2100 vlbtkquRlMj2bw9Obpel bu.h 0.128484607
2101 lmjZycGUGML2Cg7dVawT bu.h 1.401661425
2102 2fYxvQc1czvRDcT8JM7B bu.h 0.357855942
2103 mgLxjjWNdkpi3rxUB07l bu.h          NA
2104 YxsciWh4LBDtAl6lLunN bu.h 0.376943316
2105 17LEM8wPQyshaga81mOO bu.h          NA
2106 MbyIbNiCdxnuVxUZpfbH bu.h 0.447816401
2107 O72SSmqP7lQY9cI1IRXS bu.h 0.459862711
2108 K23r5Gciflq8ssI5pwWt bu.h 0.173499024
2109 mfB7GR4DFHz7xsbRfA2k bu.h 0.170569664
2110 JYNcLggG6xeng7vhONOS bu.h 0.226554227
2111 B2owtO4LQv4BlyKJysbj bu.h 0.213556687
2112 hsTC8X9J3MHKvrWSPSGp bu.h 0.252024225
2113 O64buZkTfWbzctpTJb8y bu.h 0.532495949
2114 etnyKJ3NA8vLJWQgHIDJ bu.h 1.634104413
2115 NEzK3RrTXr3H711C5ner bu.h 0.231558903
2116 hI7JpYtbCLvNfC1JPRxp bu.h 4.585844620
2117 sVuAubUTodW3ARhRN1ZZ bu.h 0.118918933
2118 QakFVrMCqt7gRZVH3iEC bu.h 0.958838275
2119 GYv2yIEyt3cnKUR2SAOh bu.h 2.131358288
2120 QQA4FUKgerUT0TLEFFgK bu.h          NA
2121 NoyW2xa7rP3UAUmSMeQr bu.h 0.211208581
2122 Ps3jaWnRHhoNNeL3PDa4 bu.h          NA
2123 3yF7oWlrwewt1IlgfOdR bu.h 0.176511268
2124 WgRfEmiRKt0bi4Pa8GXf bu.h 0.569260357
2125 CVmfiQuobm1ux4zBp3l4 bu.h 0.253338712
2126 QbBol6zjGvxaMPTAhCtK bu.h 0.857827608
2127 3b8X79WU0YWVcsmFGeEH bu.h 0.198307154
2128 z6Lh4IOHDaSwgJPnXM9y bu.h 0.371460193
2129 wSixs1RYkv1efoymvtW1 bu.h 0.577098889
2130 tnvKkJpoe2VvoCKQ2eec bu.h 0.230275949
2131 D8fTXJ4pmCgPKwoIaToD bu.h 0.472956836
2132 2944N9XVkkvQCbEFXOGW bu.h 0.209974023
2133 udX2082mfDPeFUNFECtx bu.h 0.225929392
2134 TJQ9j6A05f8ld8OK8Mzc bu.h          NA
2135 jtvtTOMSmoqa3cP4vWFw bu.h          NA
2136 KDppLvc9sRPP1RRTXUbx bu.h 0.458265009
2137 FIa2u0iyUOqpLnznCwg7 bu.h 0.055093319
2138 tKTrELf8YVSKMU6GH0tn bu.h 0.689731485
2139 QTIBd0LPhjpEAg2t8jUc bu.h 0.133021181
2140 CidpNDdCta3wU0QBEAaC bu.h 0.382091691
2141 2mOtyqwcjao4GA2ABxyL bu.h 0.324646815
2142 EgcARy4I4dqxRFy1uljh bu.h 0.807949788
2143 3lGsczpaOxmDeZmuT7yz bu.h          NA
2144 7AlcRBY2aNYzLP9XgiiD bu.h 0.386586141
2145 nT7TY7TedDb0ioGYwJu2 bu.h 0.008165302
2146 ifcG7cR8IYUd48e11ERX bu.h 0.463107194
2147 xVa4sxiVQcwRAFeHBw6Q bu.h 0.571098869
2148 piTJXDJA6kamnbAqQVSc bu.h 0.278319390
2149 3uJI4pjYUgXyX1Hk2qIL bu.h 1.059754198
2150 1EicOlsQbZR2weQYUSE4 bu.h 0.199146383
2151 AdHVDX69oRK1VBVAabUp bu.h 0.460287469
2152 oXDopHMLUvv9ZYWc4WY0 bu.h          NA
2153 jI8rOnEGaJSjTVGKc7CZ bu.h 0.369985386
2154 fiH0siERVL8DFEsALoWe bu.h 0.405211152
2155 INJiFcjen2bPWcOF8EMm bu.h 0.162746582
2156 tQpvMJzeFXX0gCrC6vyK bu.h 0.143952648
2157 TvYtzJkmM6MuLbSXfR4V bu.h 0.558913331
2158 OmZM4S3itebyEHtD50je bu.h 0.141742963
2159 vRyji87MB90B1qMDZPJN bu.h 0.421246836
2160 B5WG1QJDTZpBmQeLAwN1 bu.h 0.316716956
2161 vSxwOckngCvrG4n3EbUf bu.h 0.118088126
2162 szIdrim2GNXX9WwHE4Fy bu.h 0.200820254
2163 YgxWHqtXkNX2ukZ9kiCy bu.h 1.148338129
2164 U9nsf4r6DuxRFPnEoBKH bu.h 0.116323871
2165 iWkqE8VcvCk5Sr80QPjo bu.h 0.104003560
2166 XNnSYvWVz1rYeszmhtm8 bu.h 0.303731333
2167 syj9XP7tS6fLXa3zk0lr bu.h 0.261876124
2168 KdXV17I3V0fPvMAidxdb bu.h 0.148470608
2169 bE0XffsyPxZrgbW0vsF8 bu.h 0.322519882
2170 7prg1cG0rIH53Z3B960Y bu.h 2.178964730
2171 hf6J6yzRivfwR2Elyo9N bu.h 0.226554227
2172 XGaVXviFC59TLyx7AKLq bu.h 0.721872074
2173 N8mhQdi0Ckv2LWNLk81f bu.h 0.363154298
2174 5PP2t16GSzChbtte5vVw bu.h 1.155487212
2175 izXbHR0eYoPO82zpM9ci bu.h 0.785970020
2176 E89vWsrHcs2BX0H9x10k bu.h 0.329285308
2177 j0nw3vkMwyvjJk5uLKTs bu.h 0.495108289
2178 2djLXKQxVrpFT6EMBVel bu.h 0.186714710
2179 ZWrbw4NnMzB5thxdUE70 bu.h 0.393389958
2180 za6MrfAimgv3r4mlD3GD bu.h 0.193553422
2181 FbszVu1NkNjugVYIkYSq bu.h 0.122272302
2182 RIcuhrLt8BIs1le0DQl9 bu.h 0.638093478
2183 f0Qhy4BBQMm7haZW9njV bu.h 0.287197908
2184 U3xR5fFywsrVpOBA8QLl bu.h 0.226554227
2185 ghnnf7NOCbptKwZqbJP0 bu.h 0.226554227
2186 HySpCCsc6Z24bjQNY5WL bu.h 0.152292711
2187 Y4piZj3qKcsxJWYZlPUu bu.h 0.455477043
2188 w5ROjuO9oQmzFgChYwnN bu.h 0.263310860
2189 kovEJOLHZyADSgwnqPga bu.h          NA
2190 ZafHADISIiTJh88ymVWB bu.h 1.540479509
2191 NneQhA1SAtPySGJTD5kT bu.h 0.147163128
2192 EsnHWHltxFFWBvnaQ7wt bu.h 0.139058918
2193 MF7ywseHC6Lpx2uLgz7R bu.h 0.310960726
2194 MJzHLwX8uDBLf5PVpvm3 bu.h          NA
2195 WvS7v3OnokNVBRlGLawD bu.h 0.125409080
2196 gMVww8j5DaVLpC3Yj8nr bu.h          NA
2197 ZyEbe7eQxt4YlalJLb89 bu.h 0.319433713
2198 LGc1FR04HUEsBPr46ymO bu.h 0.523592013
2199 ZcTx9ee6nFJHzT0GdDGB bu.h 0.123799858
2200 Q6i1DNEvpyBBDntIgDg4 bu.h 0.378137165
2201 sPOsOxzbf57Cpl4JzYRp bu.h 0.264659082
2202 oy61qtPF4pg4R3ABVbJj bu.h          NA
2203 LxtXIECwYcSxnNetzXg7 bu.h          NA
2204 TnkZ3dTyO65eSD5NNCjV bu.h 0.447485537
2205 5YbhiAWE4VtpOkgYKiOV bu.h 0.395019802
2206 5a0sZZG2EX1SidVr8pTr bu.h 0.463671391
2207 BgX9DZHT7atuF2UAoakM bu.h 0.256574832
2208 kOdPwuboub15eEeAr6EV bu.h 0.172466051
2209 XcEGK0YT24vUTt758M2g bu.h 0.782924897
2210 0REiwYHX47zX4qbePYjN bu.h          NA
2211 vt916IZuBW8BbCO8LZhg bu.h 1.200725935
2212 CbkDGfCW3DtUwMDx16uM bu.h 0.297697507
2213 6d6gzVA98GwteUHuM56j bu.h 0.290476125
2214 UHNhZWUf2dWUy4LM6hpE bu.h 0.231196938
2215 RoIHSTba58R45QQst0Ei bu.h 0.083024786
2216 3kFgrHerUTRPCEMhprpx bu.h 0.391244943
2217 2YA4KcXYzGRjkRBd5PL9 bu.h 0.123790200
2218 Z0GzxSwCT4IquCK3XqV3 bu.h 0.198264384
2219 8FYM23YbIrj3Ofx7LMoo bu.h 0.232555428
2220 9pEwavS3W13KgQTb81Oi bu.h 0.321630939
2221 ym15ZZj8Df0uNAuodXn8 bu.h 0.314783496
2222 82Zq2FjvSCEGrSz97ocz bu.h 0.826959328
2223 XZ6kYLrylUTUVmi9Moii bu.h 0.369001824
2224 jnddVA5OnRXjgyBHLhkr bu.h          NA
2225 DH0T6zFub589qUF0B702 bu.h 0.285339813
2226 ArWJ1aLAwP2t66pdDGqy bu.h 2.312327593
2227 7g48cSLs8jYPIKalXiUZ bu.h 1.426787084
2228 8JaYb3F1ROCzvHGVhkAk bu.h 0.253887924
2229 wQtadQgeGBUtR1EtHfet bu.h 0.221537460
2230 Omi5XdQydF17Y0ds4gmO bu.h 0.140811721
2231 ia068eboWvrxspWSSUh4 bu.h 0.169303186
2232 SNsBrz8Txh3QpNgGXGtE bu.h 0.194286479
2233 9YaeRGOTkxRml6BYldlV bu.h 0.626568427
2234 qy5e2BKyVAdHLE9Ft3KP bu.h 0.399827257
2235 wWq9wLHxof075cOrUFE6 bu.h 0.436938201
2236 jZF8v6PoTY6RXiFVy6gY bu.h          NA
2237 B5xt3fvqtOgMSQdQDwRx bu.h 0.221272120
2238 3LlGLQjPsYvg4P5YSea5 bu.h 0.886647329
2239 ar22SP3dSDsN5foji2zA bu.h 0.133950920
2240 yceiAnl0ceWjKibWwUt8 bu.h 0.335105826
2241 syaOSO9GuFLWojMPYoT5 bu.h 0.226554227
2242 tWVab4P75BG6pBVotEpy bu.h 0.503939944
2243 n76EcPNePczVQ85qQtbB bu.h          NA
2244 umPuWugT1xnDE3i5CyiS bu.h 0.716957552
2245 NHXjhF1eF9JpU0wnqdA1 bu.h 0.584242803
2246 sDpNHlYjovuecpifbRww bu.h 0.292636180
2247 WIgOURivC4n0cmwPOXBA bu.h 0.392065644
2248 wKn6WnNZkv5B4tkpV8uu bu.h 0.564878418
2249 FyQk8RCQanKuLqIoDMwE bu.h 0.105876610
2250 3G6evfXk2aaHFUO4NsWt bu.h 0.602939771
2251 Lln9o6Ma46KYfVigvXR8 bu.h 1.344121279
2252 wdljDfBFP8vSh8MQ2Nfb bu.h 0.062435879
2253 MSGpC65S0iGZazje8tlR bu.h 0.260376680
2254 l2wX94yqLI5YY41Eg04N bu.h 0.470966294
2255 TdV5vQ3OshqNfOa56axT bu.h 0.226554227
2256 YKfl7gWHxWULS9tPXFrs bu.h 0.207366295
2257 8dnNzoCXB1BhEOgzIafL bu.h 0.159831440
2258 PHhwWi1zxc7KS7rrgWg9 bu.h 0.427581753
2259 tGZegyyiWppuB0tUkXWz bu.h 0.275752759
2260 OcE6oQcbHzuzftyyrdB6 bu.h 0.424542537
2261 jreiAkqFiypqzeLGlilx bu.h 0.452803737
2262 Qr9Jo7wwBKk9VcRGOXsQ bu.h 1.814297140
2263 SA6j8d7euDro6XzatjNQ bu.h 0.358080806
2264 XYf3SepcvILnlDs8WTBG bu.h 0.603192228
2265 xgX9LZbxnEJvwHKfw0AT bu.h 0.433252123
2266 1E9KZrTTpuJ3KjJa8cDV bu.h 0.243953429
2267 mlhVFjOIFIcEw15BvvUI bu.h 0.409233661
2268 ZXZt7x4rQtyCuV68Yi6b bu.h 0.190580312
2269 pFp0W0oC9Ym2PNt90vzA bu.h 0.197352809
2270 jtLHDBCHTQmFdAKk960m bu.h 0.124767627
2271 KyUoAik8BpKEx6tAQ726 bu.h 0.389330592
2272 qcx2RIuXihoDjqT84xfl bu.h 0.285111790
2273 5Jqzges6X72YJR7IVU7C bu.h 0.124766979
2274 fRYcCiEcUwRJIA0mQ2o7 bu.h 0.442941406
2275 grL21e3AUttmXE9Ahkux bu.h 0.645858111
2276 9wTkbsbFsfFH5zqcCBuc bu.h 1.012157075
2277 54TEF4eZN23KZglG8kHT bu.h 0.166389643
2278 z8N4Pa7qb9TeTYKUwAeQ bu.h          NA
2279 tRvesqPE5A0iKVY6G8g8 bu.h 2.707687515
2280 VpHBzD0cNZ0G63Z3WZbn bu.h 2.606581946
2281 M0Uj6L1r76s383Di6apI bu.h 1.571172133
2282 psLDhepHETw4nS6WsOGq bu.h 0.482543103
2283 21eJs1O3lWsEI15RTa48 bu.h 0.331683910
2284 8JPlYoclZmsdBMbSSwUV bu.h 0.266565168
2285 g3iCPU6zUjzgEz6cwB61 bu.h 0.183063889
2286 9aDaI43JOLXyWCFRzVo5 bu.h 0.553130673
2287 6uMXuuY18it6mt78sREJ bu.h 0.140409153
2288 A4b1mpsMcrAeuvFB9ZEM bu.h 0.876379739
2289 R1sGzUZNWuEyAXcRhhWb bu.h 0.139291970
2290 LobHXXnfhsExJHBeznFT bu.h 0.181210724
2291 IB0qJtGPs6xL8JRpYuBt bu.h          NA
2292 GexzYjhJP1cjPlyIYmBj bu.h 0.550197858
2293 jmSta4zKv7uVTnV75Qym bu.h 0.149741761
2294 IwtSIMOyuZcrJJ2dSide bu.h 0.284687588
2295 uQsTKluXIBBL8fQW9XnR bu.h 0.084835909
2296 r0J8vYU2GVNM7AaMntD4 bu.h 0.208161460
2297 KvbsDOTlLmazDnKf7jdj bu.h 0.496704167
2298 MsFmP6wdnCIMDOShVnvZ bu.h 0.293124968
2299 Yjyw7yxk1JfumMK3kuLP bu.h 0.371355525
2300 076BrNISK0IP8PsESVQX bu.h 0.146133277
2301 713sXb02mkrHAlE48iCz bu.h 0.202131431
2302 rHuRrq19Qf7W491h1pSZ bu.h 0.730252336
2303 roz2c3opqAFnyNPbzR3h bu.h 0.256775908
2304 srkAQIQzLKHWoGWrdXiz bu.h 0.143554469
2305 uFa2AxLMcTcWQza7yFWZ bu.h 0.183353981
2306 j3wPPP2j4S3VgnpmBFvH bu.h 0.419903922
2307 p9lkjNp3qQogD4OUaedm bu.h 0.448243266
2308 CW8zKiKJE434idpLxkzM bu.h 0.337020284
2309 SnRg4VW74nsig9pvR2r9 bu.h 0.330220972
2310 UTVQBHU3VaGvVYCLcwsS bu.h 0.983864806
2311 qUSXrRXrb45EnRXm0CZK bu.h 0.129072482
2312 cEus0bkT55lQZjQ0kWT5 bu.h 0.222884656
2313 CtxaA7whhGveyJhS5DQe bu.h 0.743301408
2314 zoxX2w6t4KHJ9jlRK4GI bu.h 0.371635784
2315 rfhrXlAv1kmfzwFzjCuj bu.h 0.113445300
2316 6Bnbr5wfMsSoEwr4RmmY bu.h 0.416508004
2317 bjusHaMvMJEVS8XmZ80t bu.h          NA
2318 bKtiD8u9UlxKRlobK18T bu.h 1.206279960
2319 wj0wxrv82fTB9XrX8A3i bu.h 2.585516288
2320 Aqp9HI94q4KffiYZI489 bu.h 0.119011956
2321 b8M0JyK1URtbzEoC6nsg bu.h 0.949581478
2322 lTh2mTi7deayrBw0FqFF bu.h 0.365229202
2323 mgH3X8YHuyVo5NqXdSaj bu.h 0.235423228
2324 cGWuqesye3WK7ieL0ifh bu.h 0.524788619
2325 swOv8V46B4RZrXYTCYCb bu.h 0.415551287
2326 UQJfChjDGbHBK9ydZuBL bu.h 0.383223972
2327 BWrEZMl6F6fPf8Cfark7 bu.h 1.039925272
2328 1wKWKuWmqqmvNAmw1RqQ bu.h 1.041632613
2329 dFi2sGOS1gEWWg2KDXcs bu.h 0.421285486
2330 fGcn13Dce53Ic5t7KUSy bu.h 0.209446845
2331 I39nu6yFKdVI2LWsWJc2 bu.h 0.348008363
2332 5EPXWl4PDme2kqXHiOBB bu.h 0.152062414
2333 w1mlcyU3jmDPqAIXSuV2 bu.h 0.212504612
2334 jOtySWDeJ7rf9lxtAQzX bu.h 0.834574159
2335 XKHexzMhTJJjSCCbZYAW bu.h 0.174309634
2336 9eUKJ1MDRhFJ0kdt2ikq bu.h 0.207654702
2337 fRN2BqpepgaxT82ExHgD bu.h 0.129457582
2338 JwtAapnTB0iT2CwsIkL0 bu.h 0.623831830
2339 T8uRDr68JjJ4BQT3iIty bu.h 1.192952159
2340 0vmPFl2IZJK1p5l9dGu5 bu.h 0.206518302
2341 puSjjud5MPEJwWtnleFz bu.h 0.370811290
2342 vBfwq9pNF3ugXH7QjyHJ bu.h 0.227309928
2343 VwS8sYZG8uSO6JdgG6Vg bu.h 0.521839779
2344 FMzsqWwp0Rj2W65ySPuI bu.h 0.152054139
2345 7QimZNgKB6AsgWrEexib bu.h 0.083343276
2346 P0EzPtCjGga796TdH5Dy bu.h 0.226554227
2347 WTslF06a8nHFUAWr3vIT bu.h 0.122093504
2348 ZoY30WXpfe2KoDooCJGY bu.h 0.900243116
2349 gxLCq0J0kllMVqQr7XUA bu.h 0.074426827
2350 g3iMJxodMUlpO2mQ8tOt bu.h 0.412277584
2351 VwvZLU4OilnAvOArxYc7 bu.h 0.630610528
2352 yXJcaIASTUm1jt5BV2Mm bu.h 0.284687588
2353 ZIxkkCoVniXPslnfN5ix bu.h 1.235212273
2354 ucHtUpXPYXyyiAHjMLKD bu.h 0.093370346
2355 yzpvkuUEteiwEY5CHRrG bu.h 0.208915585
2356 RFOrWXAQX1PO0HFZwlLL bu.h          NA
2357 9NyaXPkKlfyjubys7IoE bu.h 0.131351821
2358 GtHY42M30PBrlLdcosBf bu.h 0.206588075
2359 lJy6o1qMs4RSnE9I9egV bu.h 0.747491817
2360 z0vuKL7cyHyK1USkklpb bu.h 0.432846778
2361 fZqqnVNdLD3dJmD2f5nj bu.h 0.255733791
2362 yMeSz76Rr9eoJcLNUcyC bu.h 0.289904875
2363 gatwqilBIzbvfLuxsyf3 bu.h 0.122819069
2364 CAcC5hbZvjs1aY7hCZdQ bu.h 0.103212676
2365 OeTKLsuBUOqubD9ThGeg bu.h 0.275580801
2366 xoO6skepz0KramQLSPL9 bu.h          NA

Ok something’s at least a little off. I’m thinking now that the \(R^2\) rate shouldn’t be the resulting \(R^2\) divided by the new total time but instead the \(R^2\) gain divided by the gain in time? Are these the exact same thing?

It’s tweaking me out that the \(R^2\) decreases throughout. Is that saying that maybe some items are so noisy that they’re making the task a worse predictor? Is this a sum score issue?

Code

data.frame(y = rnorm(100)) |>
  mutate(x1 = y + rnorm(100, 0),
         x2 = y + rnorm(100),
         x3 = y + rnorm(100),
         x4 = y + rnorm(100),
         x = x1 + x2 + x3 + x4) |>
  lm(y ~ x, data = _) |>
  summary()


Call:
lm(formula = y ~ x, data = mutate(data.frame(y = rnorm(100)), 
    x1 = y + rnorm(100, 0), x2 = y + rnorm(100), x3 = y + rnorm(100), 
    x4 = y + rnorm(100), x = x1 + x2 + x3 + x4))

Residuals:
     Min       1Q   Median       3Q      Max 
-1.35321 -0.32090  0.03899  0.29478  1.30901 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.01650    0.04976   0.332    0.741    
x            0.20598    0.01133  18.176   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.4878 on 98 degrees of freedom
Multiple R-squared:  0.7712,    Adjusted R-squared:  0.7689 
F-statistic: 330.4 on 1 and 98 DF,  p-value: < 2.2e-16

Code

data.frame(y = rnorm(100)) |>
  mutate(x1 = y + rnorm(100, 0, 10),
         x2 = y + rnorm(100),
         x3 = y + rnorm(100),
         x4 = y + rnorm(100),
         x = x1 + x2 + x3 + x4) |>
  lm(y ~ x, data = _) |>
  summary()


Call:
lm(formula = y ~ x, data = mutate(data.frame(y = rnorm(100)), 
    x1 = y + rnorm(100, 0, 10), x2 = y + rnorm(100), x3 = y + 
        rnorm(100), x4 = y + rnorm(100), x = x1 + x2 + x3 + x4))

Residuals:
     Min       1Q   Median       3Q      Max 
-2.61038 -0.71284  0.00575  0.76361  2.75046 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)   
(Intercept) -0.160735   0.100581  -1.598  0.11325   
x            0.025364   0.009361   2.710  0.00795 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.005 on 98 degrees of freedom
Multiple R-squared:  0.0697,    Adjusted R-squared:  0.06021 
F-statistic: 7.342 on 1 and 98 DF,  p-value: 0.007952

Code

v2_dat |>
  summarize(.by = c(subject_id, task),
            sscore = first(sscore),
            score = mean(score)) |>
  pivot_wider(names_from = task, values_from = score) |>
  lm(sscore ~ dn.c + bu.h, data = _) |>
  summary() |>
  pluck("r.squared")

[1] 0.3495512

Code

# todo: conflict-harmony pairs
ranked_items <- data.frame(tank = as.numeric(),
                           task = as.character(),
                           item = as.character(),
                           test_cor = as.numeric(),
                           total_time = as.numeric())

repeat {
  pmap(list(it = v2_dat |> pull(item) |> unique()), \(it) 
       {
         v2_dat |>
           filter(item %in% it | item %in% pull(ranked_items, item)) |>
           summarize(.by = c(task, subject_id),
                     task_score = mean(score),
                     sscore = first(sscore),
                     time = first(time)) |>
           drop_na() |>
           # to-do: change to lm()
           summarize(it = it,
                     test_cor = cor(task_score, sscore),
                     time = first(time))
  }) |> 
    list_rbind() |>
    mutate(cor_rate = test_cor / 
             sum(ranked_items |> pull(total_time), time, na.rm = T)) |>
    filter(cor_rate == max(cor_rate)) |>
    mutate(rank = nrow(ranked_items) + 1,
           task = str_split_i(it , "_", i = 1),
           item = it,
           .keep = "unused",
           .before = everything()) |>
    mutate(total_time = sum(ranked_items |> 
                              arrange(total_time) |>
                              pull(total_time) |>
                              last(), time, na.rm = T)) |>
    rbind(ranked_items) -> ranked_items
  
  if ((ranked_items |> pull(time) |> sum()) > 1) break
}

ranked_items

# dn_top_items |>
#   filter(conf_item == 8 | harm_item == 8) |>
#   select(conf_item, harm_item)

Search Algorithm: FPT Version 2

Methods

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