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 <strong>Full Changelog</strong>: 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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.051 | 0.124 |
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; both teacher heads 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".