Impact of the PSR J0740+6620 radius constraint on the properties of high-density matter: Selected quantiles of microscopic and macroscopic variables from NSEOS inference
Bibliographic record
Abstract
We provide data needed to reproduce the main content of figures 1, 2, and 6 in our paper Impact of the PSR J0740+6620 radius constraint on the properties of high-density matter. Quantiles.zip inflates to Quantiles, a directory housing data, a README.md, and an example python script (plot_quantiles.py) giving examples of plotting the data stored in the quantiles CSV files. See the README.md, for more information. The data is stored in CSV files in the `Quantiles` subdirectory which holds quantile information for variables at selected values of another underlying variable. These quantiles are determined by distributions on the nuclear equation of state, computed from priors given by nonparametric gaussian-process regression techniques of Landry and Essick(2019), and likelihoods based on astrophysical observables (see Landry, Essick, and Chatziioannou 2020). The quantiles represent a property of the distribution on EoSs, and are not the posterior distribution themselves, see the note below. Nevertheless, these quantiles can be used to construct symmetric credible intervals for quantities of interest. For example, the pressure-baryon density quantiles could be used to construct a symmetric credible interval for the pressure of nuclear matter at nuclear saturation density, an important quantity in nuclear physics.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".