Bibliographic record
Abstract
Summary: Added experimental methods for using xarray to access zarr-ified tide models from the cloud. Added some performance enhancements to the interpolation routines. Improved the simplified body tide calculation and added a wrapper to the compute module. The body tide functions can also now use a choice of tide potential catalogs, including ones taking into account the relative positions of the 5 closest planets. Improved the documentation and added some admonitions to highlight tips and warnings. Itemized Changes: docs: convert README to markdown (#450) docs: update contribution guidelines (#450) docs: add some admonitions (#450) docs: add more notes and admonitions (#451) docs: update contribution guidelines (#451) refactor: use timescale shortcut wrapper function (#453) feat: add functions for converting models to xarray (#455) docs: add s3 recipes and examples (#455) chore: add aws feature dependencies to pixi (#455) chore: update pixi lock file (#455) docs: add pixi task that can create HTML documentation (#455) feat: use vectorized linear interpolator in infer_minor (#455) feat: improve performance of bilinear interpolation (#455) test: add xarray tests (#455) ci: add xarray to test environment (#455) feat: add basic xarray accessor for tide model data (#456) test: add xarray accessor checks (#456) feat: added option to gap fill constituent grids (#457) docs: add gap fill notebook to examples (#458) fix: suppress crs warnings about proj conversion (#458) test: add inpaint gap filling test (#459) feat: add Woodworth tide potential tables (#459) test: add check if running tests on GitHub Actions (#460) docs: add project testing readme (#461) feat: can choose different tide potential catalogs for body tides (#462) refactor: remake tide potential catalogs to include degree (#462) feat: include complex latitudinal dependence (#463) feat: add wrapper for catalog-based solid earth tides (#464) refactor: split IERS ephemeride method into a separate function (#464) docs: update solid earth tide description (#464) Full Changelog: https://github.com/pyTMD/pyTMD/compare/2.2.7...2.2.8
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.666 | 0.597 |
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".