xclim: xarray-based climate data analytics
Bibliographic record
Abstract
Contributors to this version: Trevor James Smith (@Zeitsperre), Pascal Bourgault (@aulemahal). New features and enhancements Added the op keyword to the growing_season_{start|end} indices and indicators, allowing for customizable threshold operators using indices.generic.compare(). (GH/1794, PR/1796). xclim now separates the optional dependencies into dev and docs recipes. Both can be installed with the all option ($ python -m pip install xclim[all]). (PR/1806). Bug fixes Units of degree-days computations with Fahrenheit input fixed to yield "°R d". Added a new xclim.core.units.ensure_absolute_temperature method to convert from delta to absolute temperatures. (GH/1789, PR/1804). Clarified a typo in the docstring formula for xclim.indices.growing_season_length. (PR/1796). Internal changes netcdf4 has been pinned below v1.7 for test stability reasons. (PR/1791). flake8-bandit-like checks have been enabled via ruff, with fixes for a few security-related issues. (PR/1806). xclim.testing.utils now employs more secure URL auditing checks. (PR/1806). CHANGES.rst has been renamed to CHANGELOG.rst, adhering to suggestions from the keepachangelog v.1.1.0 specifications. (PR/1823). CI changes GitHub repository now uses Rulesets for branch protection. (PR/1790). Version bumping and project triage is now handled by the Ouranos Helper GitHub App. (PR/1790). bump-my-version has been updated to v0.23.0. (PR/1790). The Ouranos Helper GitHub App now provides verified commits. (GH/1811, PR/1812). Added the deptry package to the dev linter tools and linting workflows for performing dependency analyses. (PR/1806). Several linting tools have been updated to the latest versions and pinned. (PR/1806).
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.155 | 0.145 |
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".