Photothermal radiometry parametric identifiability theory for reliable and unique nondestructive coating thickness and thermophysical measurements
Bibliographic record
Abstract
In this paper, we present a detailed reliability analysis of estimated parameters to a three-layer theoretical model of photothermal radiometry frequency domain signals by applying parameter identifiability conditions from two steel samples coated with ∼10 μm and 20 μm thick ceramic coating, to measure the thermophysical parameters of the coating, such as thermal diffusivity, thermal conductivity, and coating thickness. The three parameters are unique only when their sensitivity coefficients are linearly independent over the range of measurements. The study demonstrates the complexity of the identifiable experimental conditions through identifiability maps (calculated nonidentifiable locations) and sensitivity coefficient plots, even when the three separated parameters are grouped into two parameters. The validation of the reliability analysis theory by comparing the independently measured, with the fitted thicknesses of two coatings under random and optimized conditions, underscore the great importance of identifiability analysis (sensitivity coefficient plots) in the design of experiments for reliable parameter extractions, especially when the number of parameters is greater than the measurement data channels.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".