Bayesian approach to pyrometry during intercritical annealing of advanced high strength steel
Bibliographic record
Abstract
Abstract Precise thermal control during intercritical annealing is essential to the production of advanced high strength steels (AHSS). Measuring the temperature of the steel strip through pyrometry requires detailed knowledge of the spectral emissivity of the steel strip, which is imperfectly known since it varies with wavelength, direction, temperature, surface roughness, and oxidation, the latter depending on alloy composition and processing conditions. This study presents a Bayesian pyrometry methodology in which temperature and spectral emissivity are described as unknown stochastic variables that are inferred simultaneously. Additional information about the spectral emissivity obtained through ex situ characterization are incorporated into the inference through maximum likelihood priors. While standard pyrometry methods provide a point estimate of surface temperature, the Bayesian framework infers the posterior probability density, which will allow galvanizers to better assess the reliability of the pyrometrically-inferred temperature.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".