Multi-objective Optimization of Group Decision-making Based on Matter-element Extension Set
Bibliographic record
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 Resume: En raison du probleme de la prise de decisions en groupe avec l’extension set de matiere-element, le present document examine la conversion et la standardisation multi-objectif, l'extension d'association en vertu des preferences de prise de decision et l’espace de l’extension de prise de decision sans preference de la multi-dimensionnalite de prise de decision en groupe en combinant l'extension de transformation avec l'optimisation de la decision en groupe, de sorte que la comparaison et la selection d'objets dans l'environnement en changement peut etre effectuees, et des problemes systematiques de prise de decision de conversion multi-objectif et d'optimisation multi-projet dans la prise de decision multi-objectif puissent etre resolus, ce qui ameliore la precision et la fiabilite de la prise de decisions en groupe. Mots-Cles: prise de decision en groupe multi-objectif; matiere-element; extension set; extension de transformation; extension d’association
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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".