Bibliographic record
Abstract
We have investigated the crustal properties of neutron stars without fallback accretion. We have calculated the chemical evolution of the neutron star crust in three different cases (a modified Urca process without the thermal influence of a crust, a thick crust, and a direct Urca process with a thin crust) in order to determine the detailed composition of the envelope and atmosphere as the nuclear reactions freeze out. Using a nuclear reaction network up to technetium, we calculate the distribution of nuclei at various depths of the neutron star. The nuclear reactions quench when the cooling timescale is shorter than the inverse of the reaction rate. Trace light elements among the calculated isotopes may have enough time to float to the surface before the layer crystallizes and form the atmosphere or envelope of the neutron star. The composition of the neutron-star envelope determines the total photon flux from the surface, and the composition of the atmosphere determines the emergent spectrum. Our calculations using each of the three cooling models indicate that without accretion of fallback the neutron star atmospheres are dependent on the assumed cooling process of the neutron star. Each of the cooling methods have different elements composing the atmosphere: for the modified Urca process the atmosphere is $^{28}$Si, the thick crust has an atmosphere of $^{50}$Cr, and the thin crust has an atmosphere of $^{40}$Ca. In all three cases the atmospheres are composed of elements which are lighter then iron.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".