Bibliographic record
Abstract
v0.13.1 (2025-10-21) Contributors to this version: Juliette Lavoie (@juliettelavoie), Trevor James Smith (@Zeitsperre), Gabriel Rondeau-Genesse (@RondeauG). New features and enhancements New official column bias_adjust_reference. When a bias_adjust_project has multiple references (e.g., CanLead, ESPO6 v2.0), the information is stored in this column. (PR/643, PR/644). Add clip_var option in xs.clean_up to clip values outside a given range. (PR/645). Bug fixes Fixed a bug in ProjectCatalog.update where the function would crash on Windows systems. (PR/656). Breaking changes Pins have been added for h5py (>=3.12.1) and h5netcdf (>=1.5.0) to ensure that modern versions are preferably installed. (PR/658). Internal changes Updated the cookiecutter template to the latest version. (PR/651): Updated the Contributor Covenant agreement to v3.0. Replaced black, blackdoc, and isort with ruff. Added a CITATION.cff file. pyproject.toml is now PEP 639-compliant. Pinned pydantic below v2.12.0 for compatibility issues with intake-esm When running tests with tox, the h5py library is now compiled from source. This requires the hdf5 (conda) or libhdf5-dev (system) packages to be installed. (PR/658).
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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.624 | 0.774 |
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".