Bibliographic record
Abstract
Early researchers recognised that the long-established Muskingum hydrologic routing model's parameters vary with flood characteristics, but no methods were developed to account for this variation. Such methods would have made the model too complex to solve using prevailing technical knowledge. This paper presents a versatile non-linear Muskingum model with four variable parameters; each is represented by a two-step function of a dimensionless inflow variable, so the model thus has eight parameters. The model is general and produces a wide array of 15 special models with the number of parameters ranging from four to seven. The routing procedure is based on the modified Euler method. Three examples with different hydrograph types were used to evaluate model performance. Based on the application results, guidelines for model selection for different hydrograph types are presented. The recommended models have four to six parameters and substantially improve model performance for predicting flood flows compared with the traditional three-parameter model. This research continues to promote alternative thinking to improve model performance by modifying model structure, instead of the solution algorithm.
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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) | 0.000 | 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".