Bibliographic record
Abstract
The Hydrological Simulation Program FORTRAN (HSPF) model was calibrated for the South Nation watershed located in Eastern Ontario. Three nonlinear automatic optimization techniques were applied and compared: Gauss-Marquardt-Levenberg (GML) method, Random Multiple Search Method (RSM), and Shuffled Complex Evolution method developed at the University of Arizona (SCE-UA). The best GML, RSM, and SCE-UA variable values beyond which objective function improvement is insignificant were suggested. It was found that more than one parameter set is able to maintain the model in a calibrated state which reflects correlation among model parameters and equations. The lowest value of the objective function (OF) does not necessarily correspond to the optimum solution. Comparison of scatter plots, graphs of residuals, and plots of cumulative differences are required to determine the best model parameter set. Combination of Nash and Sutcliffe (NS) model fit, coefficient of efficiency, and index of agreement proved to be the best statistics for model comparison. Proper definition of the OF is crucial to successful model calibration. Over 60 single and compound OFs were compared. The OF expressed as a log of observed and simulated flows was found to be the most appropriate single OF. A compound OF expressed as a sum of squared residuals of equally weighed log-transformed maximum (top 1% of flows), minimum (bottom 20% of flows), and middle flows was found to be best for most general model applications. Uncertainty of HSPF parameters was explored by the method of moments (MM), Monte Carlo (MC) with Latin hypercube and induced correlation, and response surface (RS) methods. Typically, the MM results in the most conservative uncertainty. The 95% confidence intervals of parameter uncertainty correspond to up to 10% variations in spring maximum flows. The predictive confidence interval and predictive noise for spring maximum and autumn minimum flows using single and compound OFs were computed. The predictive intervals were computed from the 95% confidence limits of the OF. It was found that HSPF can be an efficient tool to predict flows in ungaged watersheds with parameter transfer from calibrated neighbouring watersheds. The impact of DEM resolution on HSPF topographical parameters was studied.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".