Modeling of Non-Linear Relations Among Different Design Evaluation Measures for Multi-Objective Design Optimization
Why this work is in the frame
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Bibliographic record
Abstract
This research introduces a new approach to model the non-linear relations among different design evaluation measures and to achieve the optimal design considering these different design evaluation measures through multi-objective optimization. In this approach, different design evaluation measures are mapped to comparable design evaluation indices. The non-linear relation between a design evaluation measure and its design evaluation index is identified based on the least-square curve-fitting method. The weighting factors for different design evaluation indices, representing the importance measures of these indices in the multi-objective design optimization, are achieved using the pair-wise comparison method. A case study example of automobile caliper disc brake design considering 4 different design evaluation measures is given to illustrate the effectiveness of the introduced approach.
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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 | 0.001 |
| 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 | 0.001 |
| 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