xclim: xarray-based climate data analytics
Bibliographic record
Abstract
Contributors to this version: Juliette Lavoie (@juliettelavoie), Pascal Bourgault (@aulemahal), Trevor James Smith (@Zeitsperre), David Huard (@huard), Éric Dupuis (@coxipi). Announcements To circumvent issues stemming from changes to the frequency code convention in `pandas` v2.2, we have pinned `xarray` (< 2023.11.0) and `pandas` (< 2.2) for this release. This change will be reverted in `xclim` v0.48.0 to support the newer versions. (`xarray>= 2023.11.0` and `pandas>= 2.2`). `xclim` v0.47.0 will be the last release supporting Python3.8. New features and enhancements ``New functions xclim.ensembles.robustness_fractions`` and ``xclim.ensembles.robustness_categories``. The former will replace ``xclim.ensembles.change_significance`` which is now deprecated and will be removed in `xclim` v0.49.0. (PR/1514). Added indicator ID to searched terms in the indicator search documentation page. (GH/1525, PR/1528). Bug fixes Fixed a bug with ``n_escore=-1`` in ``xclim.sdba.adjustment.NpdfTransform``. (GH/1515, PR/1516). In the documentation, fixed the tooltips in the indicator search results. (GH/1524, PR/1527). If chunked inputs are passed to indicators ``mean_radiant_temperature`` and ``potential_evapotranspiration``, sub-calculations of the solar angle will also use the same chunks, instead of a single one of the same size as the data. (GH/1536, PR/1542). Fix wrong attributes in ``xclim.indices.standardized_precipitation_index``, ``xclim.indices.standardized_precipitation_evapotranspiration_index``. (GH/1537, PR/1538). Internal changes Pinned `cf-xarray` below v0.8.5 in Python3.8 installation to further extend legacy support. (PR/1519). pip check in `conda` builds in GitHub workflows have been temporarily set to always pass. (PR/1531). Configure RtD search rankings to emphasize notebooks and indicators over indices and raw source code. (PR/1526). Addressed around 100 very basic `mypy` typing errors and call signature errors. (PR/1532). Use the intermediate ``step _cumsum_reset_on_zero`` instead of ``rle`` which is sufficient in ``_boundary_run``. (GH/1405, PR/1530).
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.106 | 0.113 |
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".