<title>NDE of zinc layer on steel substrate using laser-ultrasonic SAW</title>
Bibliographic record
Abstract
Laser-ultrasound spectroscopy, a non-contact ultrasonic technique was used to characterize the Lame coefficients ((lambda) ,(mu) ) and thickness (h) of a zinc layer on a steel substrate. This characterization is based on fitting the measured velocity dispersion curve of surface acoustic waves (SAW) to the dispersion calculated one using the conjugates gradients algorithm (C.G). A short laser pulse was used to generate a wideband pulse of ultrasound and a laser interferometer was used for its detection. From a large number of echoes we identified the one corresponding to the SAW. Furthermore other useful information were obtained from these data like attenuation and surface skimming longitudinal wave. Measurements of the velocity dispersion of the Rayleigh wave were achieved up to 50 MHz. The evaluation of layer's parameters performed for similar cases, on a pseudo-experimental model, were obtained with accuracy better then 1% for (h, (mu) ) and about 4% to 6% for (lambda) .
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".