Bibliographic record
Abstract
Contributors to this version: David Huard (@huard), Trevor James Smith (@Zeitsperre) and Pascal Bourgault (@aulemahal). New indicators New indicator specific_humidity_from_dewpoint, computing specific humidity from the dewpoint temperature and air pressure. (GH/864, PR/1027) New features and enhancements New spatial analogues method "szekely_rizzo" (PR/1033). Loess smoothing (and detrending) now skip NaN values, instead of propagating them. This can be controlled through the skipna argument. (PR/1030). Bug fixes xclim.analog.spatial_analogs is now compatible with dask-backed DataArrays. (PR/1033). Parameter dmin added to spatial analog method "zech_aslan", to avoid singularities on identical points. (PR/1033). xclim is now compatible with changes in xarray that enabled explicit indexing operations. (PR/1038, xarray PR). Internal changes xclim now uses the check-json and pretty-format-json pre-commit checks to validate and format JSON files. (PR/1032). The few logging artifacts in the xclim.ensembles module have been replaced with warnings.warn calls or removed. (GH/1039, PR/1044).
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.490 | 0.556 |
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".