Bibliographic record
Abstract
New features (none!) Bug-fixes and performance fix: ignore case of file extension by @ariostas in https://github.com/scikit-hep/uproot5/pull/1465 fix: support accessing RNTuple fields by full path by @ariostas in https://github.com/scikit-hep/uproot5/pull/1466 fix: RNTuple form construction logic by @ariostas in https://github.com/scikit-hep/uproot5/pull/1467 fix: allow ATLAS-style file names for ROOT files by @zlmarshall in https://github.com/scikit-hep/uproot5/pull/1472 fix: resolve issues with ROOT 6.36+ by @ariostas in https://github.com/scikit-hep/uproot5/pull/1463 fix: change handling of anonymous RNTuple fields by @ariostas in https://github.com/scikit-hep/uproot5/pull/1469 fix: preserve structure of jagged subfields when accessing the arrays directly by @ariostas in https://github.com/scikit-hep/uproot5/pull/1476 refactor: increased code sharing between CPU and GPU interpretation in RNTuple reading by @fstrug in https://github.com/scikit-hep/uproot5/pull/1470 Other docs: add missing funding badges by @ariostas in https://github.com/scikit-hep/uproot5/pull/1461 docs: add fstrug as a contributor for code by @allcontributors[bot] in https://github.com/scikit-hep/uproot5/pull/1478 docs: add zlmarshall as a contributor for code by @allcontributors[bot] in https://github.com/scikit-hep/uproot5/pull/1477 chore: update CITATION.cff by @ianna in https://github.com/scikit-hep/uproot5/pull/1459 chore: skip broken version of fsspec by @ariostas in https://github.com/scikit-hep/uproot5/pull/1473 New Contributors @zlmarshall made their first contribution in https://github.com/scikit-hep/uproot5/pull/1472 Full Changelog: https://github.com/scikit-hep/uproot5/compare/v5.6.3...v5.6.4
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 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.629 | 0.661 |
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".