Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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. <code>percentile(p)</code> method added to univariate models that solves the equation <code>p = S(t)</code> for <code>t</code> for parametric univariate models, the <code>conditional_time_to_event_</code> is now exact instead of an approximation. API changes In Cox models, the attribute <code>hazards_</code> has been renamed to <code>params_</code>. This aligns better with the other regression models, and is more clear (what is a hazard anyways?) In Cox models, a new <code>hazard_ratios_</code> attribute is available which is the exponentiation of <code>params_</code>. In regression models, the column names in <code>confidence_intervals_</code> has changed to include the alpha value. In regression models, some column names in <code>.summary</code> and <code>.print_summary</code> has changed to include the alpha value. In regression models, some column names in <code>.summary</code> and <code>.print_summary</code> includes confidence intervals for the exponential of the value. Significant changes to internal AFT code. A change to how <code>fit_intercept</code> works in AFT models. Previously one could set <code>fit_intercept</code> to False and not have to set <code>ancillary_df</code> - now one must specify a DataFrame. Bug fixes for parametric univariate models, the <code>conditional_time_to_event_</code> 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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.033 | 0.017 |
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 it