Bibliographic record
Abstract
The dominant mechanism of energy transport through the surface layers of a typical star is radiation, i.e., photons. Transport by convection is often important below the surface, but rarely carries a significant fraction of the flux in the photosphere. Conduction comes into play in extreme cases such as white dwarfs. So it is radiative transfer that is our main focus here. It is in the domain of radiative transfer where the physical parameters of the material comprising the star are coupled to the spectrum we see and measure. We start by setting up the differential equation describing the flow of radiation through an infinitesimal volume. The integration of the equation can then be accomplished for the geometry of the situation. Unfortunately the step from a differential to an integral equation is not a physical solution to the problem because the integrand still depends on the atomic excitation of the material, which itself depends on the temperature of the material, and the radiation field in the material. Both the thermal (collisional) and the non-thermal (radiative) excitation vary with depth in the photosphere. In most applications of the theory to real stars, a numerical model of the star's photosphere is formed from which the integrand and then the spectrum of the star can be calculated. This chapter, along with Chapters 8 and 9, develops the tools for this modeling.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".