Nuevo contraste térmico para el ensayo termográfico no destructivo de materiales
Bibliographic record
Abstract
Es bien conocido que los métodos de ensayo termográfico no destructivo (ETND) basados en el contraste térmico son afectados fuertemente por el calentamiento no uniforme sobre la superficie. Por lo tanto, los resultados obtenidos con estos métodos dependen considerablemente del punto de referencia escogido. El contraste absoluto diferencial (CAD) fue desarrollado para eliminar la necesidad de escoger un punto de referencia que defina el contraste térmico respecto a un área ideal (no defectuosa). A pesar de que la técnica CAD es muy útil para tiempos cortos, su precisión disminuye para tiempos largos cuando el frente de calor alcanza la cara opuesta de la muestra. En este artículo, se propone una nueva versión del CAD, al considerar explícitamente el grosor de la muestra y usar la teoría de cuadrupolos térmicos. Se demuestra que la validez de esta nueva técnica se incrementa para tiempos largos, mientras se preserva para tiempos cortos.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".