Bibliographic record
Abstract
We present an alternate deployment of the GrayStarServer (now ChromaStarServer (CSS)) pedagogical stellar atmosphere and spectrum synthesis WWW application, namely ChromaStarDB (CSDB), in which the atomic line list used for spectrum synthesis is implemented as an SQL database table rather than as a more conventional byte-data file. This allows for very flexible selection criteria to determine which transitions are extracted from the line list for inclusion in the synthesis, and enables novel pedagogical and research experiments in spectrum synthesis. This line selection flexibility is reflected in the CSDB UI. The database extraction is very fast and would be appropriate for the larger line lists of research-grade modeling codes. We also take the opportunity to present major additions to the ChromaStar and CSS codes that are also reflected in CSDB: (1) TiO band opacity in the JOLA approximation, (2) Metal b − f and Rayleigh scattering opacity, (3) 2D implementation of the flux integral, (4) Improvement of the convergence, (5) Expansion of the exo-planet modeling parameters, (6) A red giant template model for the initial guess at the structure, and (7) General improvements to the UI. The applications may be found at the home page of the OpenStar project: http://www.ap.smu.ca/OpenStars/ .
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.113 | 0.055 |
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".