matplotlib/matplotlib: REL: v3.4.2
Bibliographic record
Abstract
This is the second bugfix release of the 3.4.x series. This release contains several critical bug-fixes: Generate wheels usable on older PyPy7.3.{0,1} Fix compatibility with Python 3.10 Add subplot_mosaic Axes in the order the user gave them to us Correctly handle 'none' facecolors in do_3d_projection Ensure that Matplotlib is importable even if there's no HOME Fix CenteredNorm with halfrange Fix bar_label for bars with NaN values Fix clip paths when zoomed such that they are outside the figure Fix creation of RangeSlider with valinit Fix handling of "d" glyph in backend_ps, fixing EPS output Fix handling of datetime coordinates in pcolormesh with Pandas Fix processing of some errorbar arguments Fix removal of shared polar Axes Fix resetting grid visibility Fix subfigure indexing error and tight bbox Fix textbox cursor color Fix TkAgg event loop error on window close Ignore errors for sip with no setapi (Qt4Agg import errors)
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.508 | 0.548 |
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".