Bibliographic record
Abstract
What's Changed Improve docs description of Constraint Expressions (include shared dimensions) by @Mikejmnez in https://github.com/pydap/pydap/pull/357 Set dimensions at Group level by @Mikejmnez in https://github.com/pydap/pydap/pull/360 Creates method to generate dap objects by @Mikejmnez in https://github.com/pydap/pydap/pull/362 serve nc4 data by @Mikejmnez in https://github.com/pydap/pydap/pull/367 updates logo file and point to it by @Mikejmnez in https://github.com/pydap/pydap/pull/366 drop docopt-ng, beautifulsoup4, lxml and others as required dependencies by @Mikejmnez in https://github.com/pydap/pydap/pull/369 get Dap objects function and a fix by @Mikejmnez in https://github.com/pydap/pydap/pull/373 update readme by @Mikejmnez in https://github.com/pydap/pydap/pull/375 Readme by @Mikejmnez in https://github.com/pydap/pydap/pull/376 Worflows by @Mikejmnez in https://github.com/pydap/pydap/pull/377 Docs update by @Mikejmnez in https://github.com/pydap/pydap/pull/378 allow repeated named dimensions by @Mikejmnez in https://github.com/pydap/pydap/pull/381 change variable to plot and decode by @Mikejmnez in https://github.com/pydap/pydap/pull/383 Remove whitespace on ci/env file by @Mikejmnez in https://github.com/pydap/pydap/pull/386 Bump mamba-org/setup-micromamba from 1 to 2 by @dependabot in https://github.com/pydap/pydap/pull/384 Update pre-commit hooks by @pre-commit-ci in https://github.com/pydap/pydap/pull/387 removes GridType from netcdf handler by @Mikejmnez in https://github.com/pydap/pydap/pull/395 Update README.md by @Mikejmnez in https://github.com/pydap/pydap/pull/396 Allow dds and DMR parser of remote datasets with Flatten groups (slashes in name) by @Mikejmnez in https://github.com/pydap/pydap/pull/399 Parse attribute elements with atomic types on root by @Mikejmnez in https://github.com/pydap/pydap/pull/403 New Contributors @pre-commit-ci made their first contribution in https://github.com/pydap/pydap/pull/387 Full Changelog: https://github.com/pydap/pydap/compare/3.5...3.5.1
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.514 | 0.566 |
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; the direct Gemma label and the distilled Codex classifier 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".