A group decision making approach in multi-criteria material selection
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a post-operation group decision making approach for multi-criteria material selection problems. In this approach, a group of materials experts are independently asked to assign their ordinal set of preferences over given design criteria. The solution process is then followed by deriving criteria weights for each designer using the revised Simos' method [1] and using them in the ELECTRE III decision making model [2]. Among different sets of ranking solutions obtained from different designers, the candidate materials that show the most stable (with least separations) and the highest ranks are considered as best compromised candidates. To account for decision separations from different designers while optimizing the rank, an overall loss function is defined for each material and used to make final group decisions. The application of the approach is shown using an illustrative example in material selection of a thermal loaded conductor cover sheet.
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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