Simulation of lake system based on multi-objective optimization algorithm and system dynamics model
Bibliographic record
Abstract
The Great Lakes of the United States and Canada are the largest freshwater lakes in the world, and people face changing dynamics and stakeholder conflicts when it comes to lake issues. The purpose of this study was to investigate the influence of multi-objective programming and system dynamics models on the optimal water level results in the Great Lakes. A multi-objective programming model was constructed to maximize benefits and minimize costs, and multiple factors affecting water level change were considered. The genetic algorithm was used to solve the model to obtain the global optimal solution. By establishing a system dynamics model to simulate the fluctuation of water level in the Great Lakes, considering the influence of climate and human activities on the water level, and further refining the water level control, the results show that the model can effectively simulate the water level change, provide an important basis for relevant decision-making, and provide an important reference for the optimal control of the water level in the Great Lakes. In addition, the results of this study can help to provide new ideas for water level control of the same type of large lakes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".