Bibliographic record
Abstract
0.16.0 Drop Python 3.4 support. introduction of residual calculations in CoxPHFitter.compute_residuals. Residuals include "schoenfeld", "score", "delta_beta", "deviance", "martingale", and "scaled_schoenfeld". removes estimation namespace for fitters. Should be using from lifelines import xFitter now. Thanks @usmanatron removes predict_log_hazard_relative_to_mean from Cox model. Thanks @usmanatron StatisticalResult has be generalized to allow for multiple results (ex: from pairwise comparisons). This means a slightly changed API that is mostly backwards compatible. See doc string for how to use it. statistics.pairwise_logrank_test now returns a StatisticalResult object instead of a nasty NxN DataFrame 💗 Display log(p-values) as well as p-values in print_summary. Also, p-values below thesholds will be truncated. The orignal p-values are still recoverable using .summary. Floats print_summary is now displayed to 2 decimal points. This can be changed using the decimal kwarg. removed standardized from Cox model plotting. It was confusing. visual improvements to Cox models .plot print_summary methods accepts kwargs to also be displayed. CoxPHFitter has a new human-readable method, check_assumptions, to check the assumptions of your Cox proportional hazard model. A new helper util to "expand" static datasets into long-form: lifelines.utils.to_episodic_format. CoxTimeVaryingFitter now accepts strata.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.522 | 0.602 |
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; the direct Gemma label and the distilled Codex classifier 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".