Research on Calculation Model and Decision-Making Mechanism of Response Efficiency of the Whole Material Chain under Dynamic Multi-Objective Optimization
Bibliographic record
Abstract
Based on the material demand forecasting model using BP neural network and particle swarm algorithm, the study builds the material whole chain response efficiency calculation model under dynamic multi-objective optimization by comprehensively considering the demand level weights of the affected area, and adopts genetic algorithm to assist the model solution in finding the optimal and decision-making.Taking an earthquake as a case for example analysis, the model in this paper can give the Pareto frontier, and combined with the weight coefficients after the transformation of the model solving results are more scientific and feasible, the demand satisfaction rate of the original model and the transformed model are 73.43% and 74.28% respectively, and the demand satisfaction rate of the affected points is improved by 4.24%, and this paper introduces the material allocation model of the demand level weights to be able to obtain better response efficiency of the whole chain of materials, which can provide important theoretical and practical guidance for the whole chain distribution of materials.
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How this classification was reachedexpand
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 | 0.003 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".