MESA inlists for the paper Quantifying systematic uncertainties in white dwarf cooling age determinations
Bibliographic record
Abstract
This repository contains the work directories for running the MESA models presented in the paper "Quantifying systematic uncertainties in white dwarf cooling age determinations". 1. Contents MESA model work directories 'wd_builder' work directory for creating initial white dwarf models 'all_compositions_fitted' directory containing Oxygen abundance profiles. 2. Requirements MESA version: r23.05.1 MESASDK version x86_64-linux-22.6.1. Proper environment variables must be set up before running MESA (see MESA documentation) 3. Directory Structure 'all_compositions_fitted' This directory contains Oxygen abundance profiles for 0.6 Msun white dwarf from various evolutionary models and asteroseismological studies. These profiles were used to inform the parameter space sampled in this work. 'wd_builder' This directory contains inlists for the MESA tool 'wd_builder'. It uses an initial composition file 'composition.dat' to create an initial white dwarf model called 'saved.mod'. 'phase_sep' This directory contains the inlists for white dwarf evolution, modified from the inlists of Bauer 2023. It uses the initial white dwarf model 'saved.mod' and creates an evolved, cooled white dwarf model at 3000K called 'final.mod'. 4. Usage Set up the MESA environment variables as per the MESA documentation. Use the 'wd_builder' directory to create an initial white dwarf model: Ensure 'composition.dat' is present in the directory. Run the MESA 'wd_builder' tool to generate 'saved.mod' Use the 'phase_sep' directory to evolve the white dwarf model: Copy or move 'saved.mod' to this directory. Run the MESA evolution script to generate 'final.mod'. 5. References Bauer, E. B. (2023). MESA inlists for white dwarf evolution. Zenodo. https://zenodo.org/records/7846751 For more details on the research methodology and results, please refer to the full paper: "Quantifying systematic uncertainties in white dwarf cooling age determinations".
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.582 | 0.526 |
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".