Evaluation of the MIKE SHE Model in a Cold Region
Bibliographic record
Abstract
Given its rapid urbanization, the Canagagigue Creek watershed, located in Grand River Basin (Ontario, Canada), is subjected to shifting hydrological patterns. Consequently, surface runoff was simulated using a watershed-scale model MIKE SHE. The model was calibrated with four years of data and validated with another four years data. Satisfactory levels of overall correlation (R2) between monitored and model-simulated monthly runoff were: 0.87 and 0.77 for the calibration and validation periods, respectively. Corresponding values of the Nash-Sutcliffe coefficient, 0.86 and 0.75, were similar. The model was also used to simulate different management scenarios, urbanization, deforestation, conversion of pastureland into agriculture, diversification of corn system into cash crops, and application of tile drainage. Urbanization showed little impact on surface runoff as increase in urbanized area was a small fraction of the watershed area. Deforestation considerably increased (11%) the total flow. Change in cropping patterns and installation of drainage system had marginal impacts on the surface runoffs. Overall, the model was able to simulate surface runoff reasonably well on monthly and annual basis, although slightly poorer on daily basis. It was concluded that the model could be used to investigate hydrological behaviour of the watersheds in cold climate.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".