Bibliographic record
Abstract
This PR integrates Zenodo with the UltraPlot repository to enable citation via DOI. From now on, every GitHub release will be archived by Zenodo and assigned a unique DOI, allowing researchers and users to cite UltraPlot in a standardized, persistent way. We've also added a citation file and BibTeX entry for convenience. Please refer to the GitHub "Cite this repository" section or use the provided BibTeX in your work. This marks an important step in making UltraPlot more visible and citable in academic and scientific publications. 🔗 DOI:https://doi.org/10.5281/zenodo.15733564 What's Changed Fix a few tests by @cvanelteren in https://github.com/Ultraplot/UltraPlot/pull/267 set rng per test by @cvanelteren in https://github.com/Ultraplot/UltraPlot/pull/268 Add xdist to image compare by @cvanelteren in https://github.com/Ultraplot/UltraPlot/pull/266 Fix issue where view is reset on setting ticklen by @cvanelteren in https://github.com/Ultraplot/UltraPlot/pull/272 Racing condition xdist fix by @cvanelteren in https://github.com/Ultraplot/UltraPlot/pull/273 Replace spring with forceatlas2 by @cvanelteren in https://github.com/Ultraplot/UltraPlot/pull/275 Revert xdist addition by @cvanelteren in https://github.com/Ultraplot/UltraPlot/pull/277 fix: pass layout_kw in network test function by @beckermr in https://github.com/Ultraplot/UltraPlot/pull/278 fix: this one needs a seed too by @beckermr in https://github.com/Ultraplot/UltraPlot/pull/279 rm paren by @cvanelteren in https://github.com/Ultraplot/UltraPlot/pull/280 Full Changelog: https://github.com/Ultraplot/UltraPlot/compare/v1.57...v1.57.1
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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.500 | 0.437 |
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".