Segway 2.0 Application Note Datasets
Bibliographic record
Abstract
Learned parameters and resulting segmentation corresponding to the analyses shown in the Segway 2.0 application note. Directory structure: <strong>GMM</strong> (datasets corresponding to the mixture of Gaussians analysis) 1-component traindir/ log/ (training log likelihood progression) params/ (learned parameters) identifydir/ segway.bed.gz (segmentation) 3-component traindir/ log/ (training log likelihood progression) params/ (learned parameters) identifydir/ segway.bed.gz (segmentation) <strong>minibatch-fixed</strong> (datasets corresponding to the minibatch learning analysis) fixed/ traindir/ log/ (training and validation log likelihood progression) params/ (learned parameters) minibatch/ traindir/ log/ (training and validation log likelihood progression) params/ (learned parameters) <strong>TSS_prediction</strong> (datasets corresponding to the TSS prediction analysis) (where k=component number=1-5, n=random start number=1-10) outputs_[date]_k/ traindir/ log/ (training and validation log likelihood progression) params/ (learned parameters) identifydir_n/ segway.bed.gz (segmentation)
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.009 | 0.001 |
| Open science | 0.011 | 0.010 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.162 |
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; both teacher heads agree on what is shown here.
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".