Monitoring of jute/hemp fiber hybrid laminates by nondestructive testing techniques
Bibliographic record
Abstract
Abstract Damage following static indentation of jute/hemp (50 wt.% total fiber content) hybrid laminates was detected by a number of nondestructive testing (NDT) techniques, in particular, near (NIR) and short-wave (SWIR) infrared reflectography and transmittography, infrared thermography (IRT), digital speckle photography (DSP), and holographic interferometry (HI), to discover and evaluate real defects in a laminate with a complex structure. A comparative study between thermographic data acquired in the mid- (MWIR) and long-wave infrared (LWIR) spectrum bands, by pulsed (PT) and square pulse (SPT) thermography, is reported and analyzed. A thermal simulation by COMSOL ® Multiphysics (COMSOL Inc., Burlington, MA, USA) to validate the heating provided is also added. The robust SOBI (SOBI-RO) algorithm, available into the ICALAB Toolbox (BSI RIKEN ABSP Lab, Hirosawa, Japan) and operating in the MATLAB® (The MathWorks, Inc., Natick, MA, USA) environment, was applied on SPT data with results comparable to the ones acquired by several thermographic techniques. Finally, segmentation operators were applied both to the NIR/SWIR transmittography images and to a characteristic principal component thermography (PCT) image (EOF s ) to visualize damage in the area surrounding indentation.
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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".