Bibliographic record
Abstract
Note: This release introduces a slight change in behavior. Previously, uproot.dask would default to step_size="100 MB" if open_files=True and whole-file-steps (limit on step size) if open_files=False. Now both open_files cases default to steps_per_file=1 (whole-file-steps) for uniformity. If you have been using uproot.dask and this version suddenly gives you large Dask partitions, use either step_size or steps_per_file to control your partition size (step_size="100 MB" is the old behavior). New features feat: add in capability for blindly splitting files into chunks for dask by @lgray in https://github.com/scikit-hep/uproot5/pull/876 Bug-fixes and performance (none!) Other ci: [pre-commit.ci] pre-commit autoupdate by @pre-commit-ci in https://github.com/scikit-hep/uproot5/pull/874 chore(deps): bump pypa/gh-action-pypi-publish from 1.8.4 to 1.8.5 by @dependabot in https://github.com/scikit-hep/uproot5/pull/873 Full Changelog: https://github.com/scikit-hep/uproot5/compare/v5.0.6...v5.0.7
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 | 0.011 |
| 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 | 0.007 | 0.009 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.498 | 0.594 |
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".