Bibliographic record
Abstract
New features feat: add TLeafC - string - writing support by @ioanaif in https://github.com/scikit-hep/uproot5/pull/940 feat: adding a very basic FSSpecSource by @lobis in https://github.com/scikit-hep/uproot5/pull/967 feat: add support for shape touching in Dask by @agoose77 in https://github.com/scikit-hep/uproot5/pull/966 Bug-fixes and performance fix: inverted axes for variances of 2D weighted histograms when transformed to hist by @ioanaif in https://github.com/scikit-hep/uproot5/pull/965 Other test: skip RNTuple test until #928 is fixed by @jpivarski in https://github.com/scikit-hep/uproot5/pull/969 test: skip if dask-awkward, an optional dependency, is missing by @jpivarski in https://github.com/scikit-hep/uproot5/pull/968 test: use file in skhep-testdata for issue #121 by @lobis in https://github.com/scikit-hep/uproot5/pull/973 chore: update pre-commit hooks by @pre-commit-ci in https://github.com/scikit-hep/uproot5/pull/964 chore: bump Python version by @agoose77 in https://github.com/scikit-hep/uproot5/pull/980 Full Changelog: https://github.com/scikit-hep/uproot5/compare/v5.0.12...v5.1.0rc3
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.667 | 0.755 |
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".