Bibliographic record
Abstract
This release fixes a number of bugs in the recent 1.102.0 release. Many thanks to everyone submitting bug reports! It also offers test releases for Linux-ARM64 and Windows-ARM64. If you try these out, please let us know your experience! ✨ Features Add an estimated time progress dialog, particularly for surfaces @ghutchis (#2328) 🐛 Bug Fixes Fix the dialog for "update available" @ghutchis (#648) Fix Windows uninstall script to run unattended @ghutchis (#650) Fix bug when file was saved even if the user hit cancel @ghutchis (#653) Fix a bug in Linux AppImage that prevented GAFF and MMFF94 force field optimizations Add some logic to handle running when the user doesn't install Python @ghutchis (#2337) Some fixes for the ARM64 AppImage @ghutchis (#2332) Fix bug in which plugin installation dialog doesn't close @ghutchis (#2331) Add a sanity check for OB force fields - make sure they work @ghutchis (#2327) Fix a bug with calling default python when pixi is not available @ghutchis (#2325) Fix a few bugs in the constraints dialog @ghutchis (#2324) For now, don't bundle pixi with the AppImage @ghutchis (#2323) 🧰 Maintenance Minimize code duplication with a checkout action @ghutchis (#2339) Update GitHub Actions to use current SHA for more secure builds @ghutchis (#2338) Add a Windows-ARM native build for testing @ghutchis (#2335) Add an ARM64 build including AppImage @ghutchis (#2326) Add build status badges for Linux, Windows, and macOS @ghutchis (#2318) Fix windows build script @ghutchis (#2315) 📚 Translations Automated translation updates @github-actions[bot] (#2333) Credits Thanks to many contributors, including: @dependabot[bot], @ghutchis, @weblate, dependabot[bot] and github-actions[bot]
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.429 | 0.491 |
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".