Bibliographic record
Abstract
bclusterTree a generic data object clustering based on binary tree similarity measurement basic usage 1. define a index order for compares of any two data elements such index order function is a kind of function for evaluate the compares order of your data when building a binary tree based on the input data that you assign to the object list. in binary tree, 0 means identical, 1 means right and -1 means left, so your index order function should produce this 3 integer values, example as: index = function(a, b) { if (abs(a - b) <= 0.1) { 0 } else if (a > b) { 1 } else { -1 } } 2. run data clustering then we could run the clustering based on the rule that you defined in the index function: x = runif(100); c = bcluster(x, index) # view data result of the clustering for(name in names(c)) { print(name); print(x[c[[name]]]); } A more complexes demo: clustering 2D points # raw data x = runif(100); y = runif(100); seq = lapply(1:length(x), function(i) list(v = c(x[i], y[i]))); we define the raw data set at first, and then we could define an index function based on the data structure that we've defined previous, example like apply the euclidean distance for the [x,y] vector: # define compares order index = function(p1, p2) { d = sqrt(sum((p1$v - p2$v) ^ 2)); if (d <= 0.3) { 0 } else if (d <= 0.6) { 1 } else { -1 } } Finally we could run the data clustering and data visualization by: # run clustering c = bcluster(seq, index); # plot result data plot(x, y, pch = 2, col = "white"); i = 1; colors1 = c( "red", "blue", "green", "yellow", "steelblue", "purple", "black", "orange", "gray", "cyan", "pink", "skyblue", "limegreen" ); for(name in names(c)) { points(x[c[[name]]], y[c[[name]]], pch=25, col=colors1[i], bg=colors1[i]); i = i + 1; } Run the example demo code, then you should get a image plot looks liked:
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.113 | 0.104 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".