Bibliographic record
Abstract
What's Changed This release updates python-FSPS to work with v4.0 of FSPS, which had substantial source code and data file changes, largely to support a new alpha-enhancement dimension in the isochrone and spectral libraries. See https://github.com/cconroy20/fsps/releases/tag/v4.0 for more details on the FSPS updates. Of particular note for python-FSPS users: The default spectral library is now C3K_LR, with R~100; the old MILES default can be reinstated using compiler flags. The BASEL spectral library is no longer available. New afe and afeindx parameters are exposed to python-FSPS; these will be 0 and 1 respectively unless python-FSPS is compiled with the AFE_FLAG=1 pre-compiler directive Values of _zcontinuous > 1 will now raise an error. The StellarPopulation.interp_ssp() method is disabled. Smaller changes A new use_lw_tpagb (default 0) controls the library used for the TP-AGB stars cloudy_dust is now a setup variable (see #230) Full Changelog: https://github.com/dfm/python-fsps/compare/v0.4.8...v0.5.0
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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 | 0.009 |
| 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 | 0.003 | 0.007 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.275 | 0.427 |
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".