Multi-objective Optimization of Group Decision-making Based on Matter-element Extension Set
Bibliographic record
Abstract
Abstract: On account of the problem of group decision-making with matter-element extension set, this paper studies multi-objective conversion and standardization, the extension association under decision-making preferences and the extension decision-making space under no preference of the multi-dimensionality group decision-making by combining extension transformation with group decision optimization; as a result, comparison and selection of objects in changing environment can be made, and systematic decision-making problems of multi-objective conversion and multi-project optimization in multi-objective decision-making can be solved, thus improving the accuracy and the reliability of group decision-making. Key words: group decision-making; matter-element; extension set; extension transformation; extension association Résumé: En raison du problème de la prise de décisions en groupe avec l’extension set de matière-élément, le présent document examine la conversion et la standardisation multi-objectif, l'extension d'association en vertu des préférences de
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".