Bibliographic record
Abstract
<!-- Release notes generated using configuration in .github/release.yml at main --> What's Changed Major Changes 🛠 Drop Python 3.10 and NumPy < 2 by @Armavica in https://github.com/pymc-devs/pytensor/pull/1253 Remove sparse.sandbox by @jessegrabowski in https://github.com/pymc-devs/pytensor/pull/1664 New Features 🎉 Add MLX backend by @williambdean in https://github.com/pymc-devs/pytensor/pull/1365 Bugfixes 🐛 Fix rewrite weakref leak by @ricardoV94 in https://github.com/pymc-devs/pytensor/pull/1660 Documentation 📖 Fix issue #1554: Preserve custom thumbnails in gallery script by @Copilot in https://github.com/pymc-devs/pytensor/pull/1649 Maintenance 🔧 Use LAPACK functions for cho_solve, lu_factor, solve_triangular by @Fyrebright in https://github.com/pymc-devs/pytensor/pull/1605 Full Changelog: https://github.com/pymc-devs/pytensor/compare/rel-2.34.0...rel-2.35.0
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.472 | 0.583 |
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".