MESA evolutionary model input/output files and boundary condition tables for the globular cluster 47 Tucanae
Bibliographic record
Abstract
Inlist files, run_star_extras.f90 files, model output (*.mod), terminal output (*.out) and history output (LOGS/history.data) for the evolutionary MESA models of members of the globular cluster 47 Tucanae from the substellar sequence to the tip of the subgiant branch. The atmosphere/interior coupling boundary condition tables (*.tbl, *_summary.txt) are provided as well. Note that regardless of file names, all boundary condition tables were calculated for the same metallicity of [Fe/H]=-0.75 dex. The models are organized by chemical composition (blue_tail, red_tail, ridgeline), then by the depth of boundary condition tables (photosphere, tau_100) and finally by the initial stellar mass. blue_tail, red_tail and ridgeline refer to the chemical compositions that approximate the blue tail, the red tail and the ridgeline of the near infrared color-magnitude diagram of the cluster, respectively. These models are described in detail in "Exploring the Chemistry and Mass Function of the Globular Cluster 47 Tucanae with New Theoretical Color-Magnitude Diagrams" by Gerasimov R., Burgasser A.J., Caiazzo I., Homeier D., Richer H.B., Correnti M., and Heyl J., submitted for publication in the Astrophysical Journal in 2023. A customized version of MESA 15140 was used to produce these models. The customizations are provided as a patch file in custom.patch.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.216 | 0.072 |
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".