Bibliographic record
Abstract
This is mostly a bug-fix release, with a few enhancements as well. The primary enhancement is the addition of version 2.3 of the Speech-Rule Engine that underlies the accessibility tools. This includes performance enhancements as well as a Spanish localization that is tied to the MathJax localization menu. In addition, the Explorer menu in the Assistive submenu has been slimmed down to remove unneeded options. Other bug fixes and enhancements include: AsciiMath has been updated to include new features that have been added in the official AsciiMathML.js file since v2.7.2 was released. HTML-CSS: Improve detection of web fonts (#517) Improve line breaking past the container width when no break is found within it (#1883) SVG: Don't lose pre-spacing in elements containing line breaks (#1915) CommonHTML: Fix width of roots containing line breaks (#1882) TeX: Remove balanceBraces option from tex2jax, which was never implemented (#1871) TeX: Make HTML id's used in \tag handling more robust (#1899) TeX: Make \DeclareMathOperator and \Newextarrow localizable by begingroup (#1876) SVG: Measure sizes of annotation-xml elements properly (#1870) TeX: Have \bigg and friends to trim spaces from their arguments (#1819) Handle default border width properly in SVG and HTML-CSS (#1855) Decode hash URI component so it works with special characters (#1843) CommonHTML: Reset character width if a reset occurs while an equation is being processed (#1837) CommonHTML: Properly scale widths in line breaking algorithm (#1881) HTML-CSS: Fix position of rightmost glyph in multi-glyph horizontal stretchy characters (#1896) MathML: Don't add duplicate xmlns attribute when original is empty (#1862) TeX: Don't produce unwanted mrows with \left...\right (#1829)
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.383 | 0.469 |
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".