Bibliographic record
Abstract
This is a minor release with a number of bug and compatibility fixes as well as a number of enhancements. Many thanks to recent @henriqueribeiro, @poplarShift, @hojo590, @stuarteberg, @justinbois, @schumann-tim, @ZuluPro and @jonmmease for their contributions and the many users filing issues. Enhancements: Add numpy log to dim transforms (#3731) Make Buffer stream following behavior togglable (#3823) Added internal methods to access dask arrays and made histogram operation operate on dask arrays (#3854) Optimized range finding if Dimension.range is set (#3860) Add ability to use functions annotated with param.depends as DynamicMap callbacks (#3744) Bug fixes: Fixed handling datetimes on Spikes elements (#3736) Fix graph plotting for unsigned integer node indices (#3773) Fix sort=False on GridSpace and GridMatrix (#3769) Fix extent scaling on VLine/HLine annotations (#3761) Fix BoxWhisker to match convention (#3755) Improved handling of custom array types (#3792) Allow setting cmap on HexTiles in matplotlib (#3803) Fixed handling of data_aspect in bokeh backend (#3848, #3872) Fixed legends on bokeh Path plots (#3809) Ensure Bars respect xlim and ylim (#3853) Allow setting Chord edge colors using explicit colormapping (#3734) Fixed bug in decimate operation (#3875) Compatibility: Improve compatibility with deprecated matplotlib rcparams (#3745, #3804) Backwards incompatible changes: Unfortunately due to a major mixup the data_aspect option added in 1.12.0 was not correctly implemented and fixing it changed its behavior significantly (inverting it entirely in some cases). A mixup in the convention used to compute the whisker of a box-whisker plots was fixed resulting in different results going forward.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.329 | 0.357 |
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".