Bibliographic record
Abstract
New features: AsymptoticLimits now supports the SALLINO method, estimating the likelihood with one-dimensional histograms of the scalar product of theta and the estimated score. Improved default histogram binning in AsymptoticLimits and added more binning options, including fully manual specification of the binning. Histograms now calculate a rough approximate of statistical uncertainties in each bin and give out a warning if it's large. (At DEBUG logging level they'll also print the uncertainties always, and Histogram.histo_uncertainties lets the user access the uncertainties.) Breaking / API changes: The AsymptoticLimits functions expected_limits() and observed_limits() now return (theta_grid, p_values, i_ml, llr_kin, log_likelihood_rate, histos). histos is a list of histogram classes, the tutorial shows how they allow us to plot the histograms. The returns keyword to these functions is removed. The keywords theta_ranges and resolutions were renamed to grid_ranges and grid_resolutions. Changed some ML default settings: less hidden layers, smaller batch size. Changed function names in the FisherInformation class (the old names are still available as aliases for now, but deprecated). Bug fixes: Various small bug fixes. Tutorials and documentation: AsymptoticLimits is finally properly documented. All incomplete user-facing docstrings were updated. Internal changes: Refactored histogram class. AsymptoticLimits is now much more memory efficient.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.278 | 0.227 |
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".