Bibliographic record
Abstract
What's Changed Re-enable build of stable docs by @nkoep in https://github.com/pymanopt/pymanopt/pull/228 Add JAX backend by @nkoep in https://github.com/pymanopt/pymanopt/pull/206 Add utility function to check Hessian operators by @nkoep in https://github.com/pymanopt/pymanopt/pull/230 Migrate from versioneer to setuptools-scm by @nkoep in https://github.com/pymanopt/pymanopt/pull/231 Add unitary group manifold by @nkoep in https://github.com/pymanopt/pymanopt/pull/232 and https://github.com/pymanopt/pymanopt/pull/233 chore: move metadata to pyproject.toml by @SauravMaheshkar in https://github.com/pymanopt/pymanopt/pull/236 Update zenodo to DOI umbrella badge by @nkoep in https://github.com/pymanopt/pymanopt/pull/238 feat(ci): add pip cache to CI by @SauravMaheshkar in https://github.com/pymanopt/pymanopt/pull/239 Add Complex valued manifolds by @antoinecollas in https://github.com/pymanopt/pymanopt/pull/125 New Contributors @SauravMaheshkar made their first contribution in https://github.com/pymanopt/pymanopt/pull/222 Full Changelog: https://github.com/pymanopt/pymanopt/compare/2.1.1...2.2.0
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.420 | 0.564 |
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".