Simulation of lake system based on multi-objective optimization algorithm and system dynamics model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 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 | 0.002 |
| 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