Bibliographic record
Abstract
Contributors to this version: Pascal Bourgault (@aulemahal), Juliette Lavoie (@juliettelavoie), David Huard (@huard). Bug fixes Invoking lazy_indexing twice in row (or more) using the same indexes (using dask) is now fixed. (GH/1048, PR/1049). Filtering out the nans before choosing the first and last values as fill_value in _interp_on_quantiles_1D. (GH/1056, PR/1057). Translations from virtual indicator modules do not override those of the base indicators anymore. (GH/1053, PR/1058). Fix mmday unit definition (factor 1000 error). (GH/1061, PR/1063). New features and enhancements xclim.sdba.measures.rmse and xclim.sdba.measures.mae now use numpy instead of sklearn. This improves their performances when using dask. (PR/1051). Argument append_ends added to sdba.unpack_moving_yearly_window (PR/1059). Internal changes Ipython was unpinned as version 8.2 fixed the previous issue. (GH/1005, PR/1064).
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.010 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.451 | 0.481 |
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".