Bibliographic record
Abstract
New features feat: add support for current RNTuple files by @ioanaif in https://github.com/scikit-hep/uproot5/pull/962 feat: refactor url - object split (motivated by fsspec integration) by @lobis in https://github.com/scikit-hep/uproot5/pull/976 feat: asyncio LoopExecutor and async fsspec source by @lobis in https://github.com/scikit-hep/uproot5/pull/992 feat: update the executor submit interface to take keyword arguments and be compatible with concurrent.futures.ThreadPoolExecutor by @lobis in https://github.com/scikit-hep/uproot5/pull/1001 Bug-fixes and performance fix: remove unused hist import in test_0965 by @GaetanLepage in https://github.com/scikit-hep/uproot5/pull/994 fix: cache form remapping to avoid per-chunk workload by @agoose77 in https://github.com/scikit-hep/uproot5/pull/998 Other docs: add GaetanLepage as a contributor for test by @allcontributors in https://github.com/scikit-hep/uproot5/pull/995 chore: add types to most of the uproot.source module by @lobis in https://github.com/scikit-hep/uproot5/pull/996 chore: update pre-commit hooks by @pre-commit-ci in https://github.com/scikit-hep/uproot5/pull/993 chore: add pre-commit formatters for toml and yaml by @lobis in https://github.com/scikit-hep/uproot5/pull/1002 New Contributors @GaetanLepage made their first contribution in https://github.com/scikit-hep/uproot5/pull/994 Full Changelog: https://github.com/scikit-hep/uproot5/compare/v5.1.1...v5.1.2
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.059 |
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".