Bibliographic record
Abstract
Bug fix A significant bug in the interpolation of SSP luminosities was discovered and fixed by @Martin-Rey in https://github.com/pynbody/pynbody/pull/901 To quote from the warning added to the documentation: Versions >=2.0 and <=2.1.2 contained a bug in the new interpolation tables, where due to a missing log, all star particles were essentially assumed to have super-solar metallicity. This was not caught by our regression tests because it was introduced at the same time as updating the SSP tables. It is fixed in version 2.1.3. The size of the resulting changes is around 10% for old star particles, but can be up to a factor of 3 in luminosity (up to 1.2 magnitudes) for star particles less than 30 Myr old. Other changes Make slightly more robust xcode version detection routine https://github.com/pynbody/pynbody/pull/902 Full Changelog: https://github.com/pynbody/pynbody/compare/v2.1.2...v2.1.3
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.281 | 0.377 |
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".