Bibliographic record
Abstract
72 label, 54 linearly not separable, 61, 62 linearly separable, 58 classification, 241 classification error, 255 classifier, 4, 53-54, 290 Bayes, 82 binary tree, 110, 122 ensemble, 78 naïve Bayes, 97 nearest neighbors, k-NN, 31, 43-47, 55 random forest, 81-82, 160-163 SVM, 31, 43, 57, 160-163 weak, 78 cluster validity index, 255 constraints, 59 cost-sensitive learning, 288 coverage, 224, 225, 227, 233, 257 coverage-specificity characteristics, 237 cross-validation, 37 data balancing, 287 excitatory, 229 inhibitory, 228 missing, 276 summarization, 199 weighted, 228, 280 data imputation, 275, 276 hot deck imputation, 277 random, 277 regression prediction, 277 dataset balanced, 20, 172 handwritten digits see MNIST under dataset handwritten letters, 121 imbalanced, 172 learning, 4, 37, 54 MNIST, 119, 121, 124, 172 music notation symbols, 119, 121, 172 oversample, 21, 119 test, 37, 55, 122, 161, 163 training, 37, 55, 122, 161, 163 undersample, 21, 120 wine, 67 decision boundary, 84 formula, 61, 63-64 region, 5, 7 surface, 84 tree, 66 dendrogram, 271 distance Chebyshev, 55, 250, 257 Euclidean, 55, 250, 253 Hamming, 250 Mahalanobis, 89 Manhattan, 55 diversity of classes, 72 entropy, 73-74 Gini index, 74-75 index of incorrect classification, 73
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.522 | 0.438 |
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".