Investigating The Effect Of A Speckle Pattern On Measurement Uncertainty In A Three-Dimensional Digital Image Correlation (3D-Dic) System
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Bibliographic record
Abstract
Three-dimensional digital image correlation (3D-DIC) is an imaging technique that uses cameras to measure the surface displacement of a speckled specimen under test loading from which surface strains can be derived. This study aims to investigate the effect of the speckle pattern on the uncertainty in the measurement system. A Monte-Carlo experimental approach is used by uniformly displacing a known speckle pattern by a prescribed amount. This allows the coupled influence of the image collection system, processing and post-processing to be investigated. To minimize the uncertainty of a speckle pattern, it was determined that uniform speckle size of 5-pixel diameter speckles at a density of one speckle per 20 square-pixels is optimal. The methods used to measure and analyze the speckle pattern effects on measurement uncertainty are presented.
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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 | 0.001 | 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) | 0.000 | 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