Convergence of a finite difference approach for detailed deviation zone estimation in coordinate metrology
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Bibliographic record
Abstract
<p class="Abstract">In order to comprehend an entire surface's deviation zone, infinite measured points are required. Using the common measurement techniques through coordinate metrology, a limited number of surface actual points can be acquired. However, the obtained points would not provide sufficient information to examine the geometry thoroughly. A novel approach to predict surface behaviour via Distribution of Geometric Deviations (DGD) is examined in this paper. The methodology governs the mean value property of the harmonic functions to solve Laplace equation around each measured point. This DGD model can be used to reconstruct surface deviation values at any unmeasured point of the inspected surface based on a limited number of measured points. The convergence of the introduced approach is studied in this paper. A complete approach to implement the developed methodology is described, and the validation process is studied using actual case studies and mathematical functions. This methodology is practical in closed-loop inspection and manufacturing processes to form a scheme for compensating the surface errors during manufacturing process based on the DGD model.</p>
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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 | 0.000 |
| 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)
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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