Multi-Method END Study in the Search for 'Defects' in an Aeronautical Radar Element
Bibliographic record
Abstract
Non-Destructive Testing (NDT) has gone from being a simple laboratory curiosity to an indispensable tool in the industry to determine the level of quality achieved in its products. The new concepts of Integrated Manufacturing (CIM) bring a more universal concept of quality compared to the past philosophy based on Quality Control as a group specialized in checking whether production works within certain specifications. Even so, NDTs have not lost interest, but have seen their interest increased due to automated inspection techniques. It has become a contribution to the structuring of quality as it allows to move from purely empirical criteria to other more objective and that constitute the link between design and evaluation (Ramirez et al, 1996). The work presented, although it does not propose new methods or techniques of NDT, has the interest of converging into a single object five conventional methods each of which provides partial information about their quality of manufacture and must synthesize the results in order to evaluate it. Furthermore, it shows a situation of the application of NDT in which these must be applied in the absence of reference standards, as they do not exist. This peculiar situation is completely different from the usual situation in the use of NDT in the industry, both in manufacturing processes and in maintenance inspections.
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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 | 0.028 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".