Bibliographic record
Abstract
The nonlinear Muskingum model has three parameters (storage parameter, weighting parameter, and exponent parameter) that are assumed in model estimation to be constant. The exponent parameter, which has no physical meaning, represents the average nonlinear behavior of the flood during the entire routing period. To address the variations of nonlinearity during the routing period, this paper considers a variable exponent parameter that varies with the inflow level. The boundaries of the inflow levels are considered to be dimensionless parameters. The problem is formulated as a mathematical optimization model that minimizes the sum of the squared (SSQ) or absolute deviations between the observed and estimated outflows. An efficient spreadsheet-based software is implemented. The proposed model was applied by using three examples involving single peak, multipeak, and nonsmooth hydrographs. The results show that the range of the optimal exponent parameters is small, yet the improvement in the fit of the nonlinear Muskingum model is substantial; the SSQ reduction reaches 35%, compared with the case of a constant exponent parameter. The proposed model should be of interest to researchers and engineers working in the area of flood management.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".