Bibliographic record
Abstract
This is a minor patch release to fix issues compatibility with the about to be released Bokeh 2.0.1 release. Additionally this release makes Pandas a hard dependency, which was already implicitly the case in 1.13.0 but not declared. Lastly this release contains a small number of enhancements and bug fixes. Enhancements: Add option to set Plotly plots to responsive (#4319) Unified datetime formatting in bokeh hover info (#4318) Allow using dim expressions as accessors (#4311) Add explicit .df and .xr namespaces to dim expressions to allow using dataframe and xarray APIs (#4320) Allow defining clim which defines only upper or lower bound and not both (#4314) Improved exceptions when selected plotting extension is not loaded (#4325) Bug fixes: Fix regression in Overlay.relabel that occurred in 1.12.3 resulting in relabeling of contained elements by default (#4246) Fix bug when updating bokeh Arrow elements (#4313) Fix bug where Layout/Overlay constructors would drop items (#4313) Compatibility: Fix compatibility with Bokeh 2.0.1 (#4308) Documentation: Update API reference manual (#4316)
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.346 | 0.388 |
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".