Bibliographic record
Abstract
New features feat: Support reading from S3 by @veprbl in https://github.com/scikit-hep/uproot5/pull/916 Bug-fixes and performance fix: pandas and double nested vectors issue 885 by @ioanaif in https://github.com/scikit-hep/uproot5/pull/912 fix: don't assume Uproot is in global scope in TPython::Eval by @jpivarski in https://github.com/scikit-hep/uproot5/pull/927 fix: expressions failing in pandas issue 922 by @ioanaif in https://github.com/scikit-hep/uproot5/pull/930 Other chore: use 2x faster black mirror by @henryiii in https://github.com/scikit-hep/uproot5/pull/929 chore: update pre-commit hooks by @pre-commit-ci in https://github.com/scikit-hep/uproot5/pull/918 Full Changelog: https://github.com/scikit-hep/uproot5/compare/v5.0.10...v5.0.11
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.460 | 0.477 |
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".