Hydrologic modelling and prediction of extreme streamflow events in the Jock River Watershed, Ontario, Canada
Bibliographic record
Abstract
Historical and predicted extreme streamflows were analysed for the Jock River watershed, using Generalized Extreme Values (GEV) and Streamflow Threshold Level (STL) methods.The historical streamflow shows earlier snow melt peaks (~10 days), decreased snow melt peaks, and decreases in consecutive drought days (CDD), whereas the summer-fall season peaks and mean annual streamflow (MAS) have been increasing.The Jock River Watershed Model (JRWM) was developed using the Raven modelling framework and achieved a Nash-Sutcliffe of 0.76 with reduced capacities to characterize low flows.Predicted streamflow changes from climate change scenarios include snow melt peaks decreased by 50% and shifted seven weeks earlier by 2099.GEV analysis showed that extremes are decreasing by ~10% for the snow melt peaks, while increases in MAS are continuing and the summer-fall peaks are increasing by ~30%.STL analysis for CDD shows the magnitude and number of CDD events are increasing by ~300%.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".