Supplemental Materials for "Schwarzschild and Ledoux are equivalent on evolutionary timescales"
Bibliographic record
Abstract
This Zenodo repository contains a .tar file which contains datasets which can be used along with the code in the associated Github repository (https://github.com/evanhanders/schwarzschild_or_ledoux, an copy of which is also included here as a .tar file) to create all of the static figures in the paper. The figures can be recreated with this data by using the Python scripts in the schwarzschild_or_ledoux/publication_figures/ folder of the Git repository. The data are as follows: Figure 1: early_slices.h5, late_slices.h5 - 2D slices through various planes in the simulation which show the dynamics at a few early and late times in the simulation. early_profiles.h5, late_profiles.h5 - 1D horizontally-average profiles at the times associated with the dynamics in the 'slices' files. early_scalars.h5, late_scalars.h5 - files that contain some various scalar info (e.g., where the boundary is determined by the Schwarzschild and Ledoux criteria) for the early and late dynamics. Figure 2 - 1D horizontally-averaged profiles for the full simulation in the paper are output into the "merged_profiles.h5" file. The output cadence is once every freefall time, so there are roughly 20,000 time points for each profile. figure 2 uses the initial state and the state at t = 17,000. Figure 3 - scalar_data.h5 contains scalar values inferred from merged_profiles.h5 at each point in time. This file can be re-created by the user by using the 'profile_to_scalar.py' file inside of the publication_figures/ folder in the repository.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.714 | 0.272 |
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".