Interactive Methodology for Optimized Defect Characterization by Quantitative Pulsed Phase Thermography
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Pulsed phase thermography is a nondestructive evaluation processing technique based on the discrete Fourier Transform. The time-frequency duality plays a critical role in the selection of the sampling and truncation parameters and has to be addressed experimentally as a function of the inspected depth. To characterize a wide range of depths in a single test, the ideal solution is to sample at the maximum available frame rate for the longest possible time and to process all this data at once. Nevertheless, two factors restrict this operation. First, the maximum frame rate and storage capacity in any acquisition system are limited and so is the span of potentially detected depths. Second, although limited, the storage capacity generally exceeds the ordinary PC's capabilities to handle simultaneously all the collected information. As a result, a compromise between sampling and storage capacity, processing capabilities and range of potentially detected depth needs to be made. A four-step interactive methodology is proposed to deal with this problem. The idea is to perform a first partial processing with a fraction of the recorded data for visualization purposes only and then to individually manage selected (defective) areas, now visible, without repeating any test.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it