Bibliographic record
Abstract
Changelog v0.13 (2025-09-03) Contributors to this version: Juliette Lavoie (@juliettelavoie), Pascal Bourgault (@aulemahal), Artem Buyalo (@ArtemBuyalo), Éric Dupuis (@coxipi). New features and enhancements Add additive_space option to xs.train. (PR/603). Modify the xclim modules definition of relative_humidity_from_dewpoint to include invalid_values='clip'.(PR/616). Add possibility to "creep fill" iteratively with argument steps in xs.spatial.creep_weights. (PR/594). Ability to save and load sparse arrays like the creep or regridding weights to disk with xs.io.save_sparse and xs.io.load_sparse. (PR/594). Generalize xs.regrid.create_bounds_gridmapping to include dataset with crs. (PR/628, GH/627). Ability to return multiple periods if passed multiple warming levels in xs.extract.get_period_from_warming_level. (PR/630, GH/629). Update xscen to xclim 0.58 (PR/634). New function xs.spatial.rotate_vectors to rotate vectors from/to their native grid axes to/from real west-east/south-north axes. (PR/635). New function xs.spatial.get_crs to get a cartopy crs from a grid mapping variable (only Rotated Pole and Oblique Mercator) (PR/635). Bug fixes Add standard_name to dtr definition in conversions. (PR/611). Better handling of attributes in xs.train. (PR/608, GH/607) Fix dimension renaming in xs.spatial_mean. (PR/620) Bug fixes Fixed xs.utils.xclim_convert_units_to context patching. (PR/604).
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.010 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.437 | 0.569 |
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".