Bibliographic record
Abstract
What's Changed enable updating the session credentials from a different session by @Mikejmnez in https://github.com/pydap/pydap/pull/537 Add an extra condition to get_cmr_urls to use if previous returns None by @Mikejmnez in https://github.com/pydap/pydap/pull/539 fix: get_cmr_urls by @Mikejmnez in https://github.com/pydap/pydap/pull/540 Enable shared dimensions option on when consolidated data by @Mikejmnez in https://github.com/pydap/pydap/pull/541 raise exception when trying to batch multiple variables in the dap2 p… by @Mikejmnez in https://github.com/pydap/pydap/pull/544 reorganize batching to return None -> add data into pydap dataset instead by @Mikejmnez in https://github.com/pydap/pydap/pull/546 Update pre-commit hooks by @pre-commit-ci[bot] in https://github.com/pydap/pydap/pull/548 Bump actions/setup-python from 5 to 6 by @dependabot[bot] in https://github.com/pydap/pydap/pull/552 a fix to parse ces with subsets by @Mikejmnez in https://github.com/pydap/pydap/pull/556 add slice as an argument to create single dap url with multiple variables by @Mikejmnez in https://github.com/pydap/pydap/pull/554 Improves memory management by @Mikejmnez in https://github.com/pydap/pydap/pull/560 add a data checker by @Mikejmnez in https://github.com/pydap/pydap/pull/561 Improve handling dimensions for datasets with nested groups by @Mikejmnez in https://github.com/pydap/pydap/pull/563 remove global url definitions - make tests self contained by @Mikejmnez in https://github.com/pydap/pydap/pull/564 Improve thread-safety ness and caching behavior by @Mikejmnez in https://github.com/pydap/pydap/pull/566 Improve dmr parser and deserialization by @Mikejmnez in https://github.com/pydap/pydap/pull/567 Full Changelog: https://github.com/pydap/pydap/compare/3.5.6...3.5.7
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.285 | 0.363 |
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".