Bibliographic record
Abstract
Highlights of this release include: Plotting and Annotation improvements hatch parameter for pie Polar plot errors drawn in polar coordinates Additional format string options in ~matplotlib.axes.Axes.bar_label ellipse boxstyle option for annotations The extent of imshow can now be expressed with units Reversed order of legend entries pcolormesh accepts RGB(A) colors View current appearance settings for ticks, tick labels, and gridlines Style files can be imported from third-party packages Improvements to 3D Plotting 3D plot pan and zoom buttons adjustable keyword argument for setting equal aspect ratios in 3D Poly3DCollection supports shading rcParam for 3D pane color Figure and Axes Layout colorbar now has a location keyword argument Figure legends can be placed outside figures using constrained_layout Per-subplot keyword arguments in subplot_mosaic subplot_mosaic no longer provisional Widget Improvements Custom styling of button widgets Blitting in Button widgets Other Improvements Source links can be shown or hidden for each Sphinx plot directive Figure hooks New & Improved Narrative Documentation Brand new :doc:Animations tutorial. New grouped and stacked bar chart <../../gallery/index.html#lines_bars_and_markers>_ examples. New section for new contributors and reorganized git instructions in the :ref:contributing guide . Restructured :doc:/tutorials/text/annotations tutorial.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.010 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.644 | 0.671 |
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".