Bibliographic record
Abstract
In order to improve the reliability of the Storm Water Management Model (SWMM), a parameter-optimization approach is required to determine the "best" input parameter sets. Within SWMM, the RUNOFF module is the best candidate module for uncertainty reduction by parameter optimization. The Genetic algorithm (GA) method is developed to optimize SWMM RUNOFF parameters. The basic principle of the GA is the same as that which controls the genetic reproduction process with crossover and mutation being the major operations. By applying the genetic algorithm to SWMM with the aid of the sensitivity wizard in PCSWMM, a sensitivity-based method for automating the calibration of the runoff model is developed. Overall, the average accuracy of the calibrated model was within 97% of the target dataset (TD) after approximately 58 cycles of the GA calibration program. The paper covers the genetic algorithm calibration method and its accuracy, efficiency, robustness and reliability.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".