Population synthesis data for the mass distribution of stripped stars
Bibliographic record
Abstract
Here we provide the results of our population synthesis model runs used in The Mass Distribution of Stars Stripped in Binaries: The Effect of Metallicity (Hovis-Afflerbach et al. 2025). The .zip file contains four folders, for the four metallicities modeled in this study. These are Z=0.014, similar to solar metallicity, Z=0.006, similar to the metallicity of the LMC, Z=0.002, similar to the metallicity of the SMC and many galaxies at Cosmic Noon, and Z=0.002, very low metallicity. Each folder contains 100 files, one for each model run. Each file contains a header including the assumptions made in running the model, and a table with the data. The table has one row for each stripped star present when the run concludes (after 1 Gyr, when the population is in equilibrium), and the following columns: Evolution: the evolutionary pathway to the creation of this stripped star. This can be either strip_RLOF_MS (stable mass transfer during the Main Sequence), strip_RLOF_HG (stable mass transfer during the Hertzsprung gap), or strip_CEE_HG (common envelope evolution during the Hertzsprung gap). M1init: the initial mass of the primary star, in units of solar masses. M2init: the initial mass of the secondary star, in units of solar masses. star_mass_1: the final stripped star mass of the primary star, in units of solar masses. Python files to create the figures in the paper from this data, along with instructions for doing so, can be found at https://github.com/berylha/stripped-star-distribution.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.104 | 0.066 |
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".