Hydrologic modelling to assess the climate change impacts in a Southern Ontario watershed
Bibliographic record
Abstract
In Southern Ontario, the Canard River watershed is the largest subwatershed of the Detroit River watershed on the Canadian side. The Soil and Water Assessment Tool (SWAT) model was implemented in the Canard River Watershed to understand the hydrologic regime and assess the impacts of potential future climate change on the hydrology of the watershed. The SWAT model was calibrated and validated against observed streamflow data. The Nash-Suttcliffe efficiencies of the model for monthly streamflow predictions were 0.81 and 0.83, respectively, during the calibration and validation periods. The LARS-WG, weather generator was employed to generate daily future weather data at local scale using the Canadian Regional Climate Model (CRCM) outputs under SRES A2 scenario for the years 2041 to 2070. It was found from the model results that the average annual streamflow could be increased by 12% compared to that over the base period from 1961 to 1990. The results also indicated that streamflow would be increased signifi...
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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 | 0.001 | 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".