CamDavidsonPilon/lifelines: v0.22.0
Bibliographic record
Abstract
New features Ability to create custom parametric regression models by specifying the cumulative hazard. This enables new and extensions of AFT models. percentile(p) method added to univariate models that solves the equation p = S(t) for t for parametric univariate models, the conditional_time_to_event_ is now exact instead of an approximation. API changes In Cox models, the attribute hazards_ has been renamed to params_. This aligns better with the other regression models, and is more clear (what is a hazard anyways?) In Cox models, a new hazard_ratios_ attribute is available which is the exponentiation of params_. In regression models, the column names in confidence_intervals_ has changed to include the alpha value. In regression models, some column names in .summary and .print_summary has changed to include the alpha value. In regression models, some column names in .summary and .print_summary includes confidence intervals for the exponential of the value. Significant changes to internal AFT code. A change to how fit_intercept works in AFT models. Previously one could set fit_intercept to False and not have to set ancillary_df - now one must specify a DataFrame. Bug fixes for parametric univariate models, the conditional_time_to_event_ is now exact instead of an approximation.
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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.521 | 0.464 |
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".