MULTIOBJECTIVE DESIGN OPTIMIZATION BASED ON SATISFACTION METRICS
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Bibliographic record
Abstract
Abstract A formal multiobjective optimization method based on satisfaction metrics is presented for designing an engineering system with mathematical rigour. Three satisfaction-based design models with different tradeoff strategies are developed to facilitate the incorporation of satisfaction metrics into the context of design formulations. These models are derived from different combinations of satisfaction-incorporated design objectives, enabling the conversion of the original multiple objectives appropriately to a single unified goal. This makes it easy to apply any available single-objective mathematical programming solver for the resulting problem solving. Not only does the method generate a Pareto-optimal solution, but also it allows for the generation of many design alternatives in a feasible design space. A computational procedure is also suggested to guide design implementations. For illustration, an example is worked out to show the computational details and the utility of the newly developed design models.
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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 | 0.001 | 0.003 |
| 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