Distinguishing profile deviations from a part's deformation using the maximum normed residual test
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Bibliographic record
Abstract
Non-rigid parts, in free-state, may have a considerable different shape than their nominal model due to dimensional and geometric variations of manufacturing process, gravity loads and residual stress induced distortion. Therefore, sorting profile deviation from a part's deformation by comparing the part's nominal shape to its scanned free-state shape is a challenging task. This task is a key step in the Iterative Displacement Inspection (IDI) algorithm used for the inspection of non-rigid parts without the use of costly specialized fixtures. This paper proposes the use of the statistical maximum normed residual test to improve the aforementioned identification task. Thirty two simulated manufactured parts are studied to show that the proposed method reduces the type I and II identification error of the IDI method.
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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 | 0.000 | 0.001 |
| 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) | 0.000 | 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