Large-scale distributed watershed modelling for reservoir operations in cold boreal regions
Bibliographic record
Abstract
The macroscale deterministic hydrologic model, SLURP, was modified and tested on two large watersheds in northern Manitoba, the Taylor River watershed (899 km2) and the upper Burntwood River watershed (6959 km2). Calibration and validation of the model on both watersheds between 1985 and 2000 identified a number of model deficiencies and recommendations for improvement. Date-dependent snowmelt rates were replaced with a single constant snowmelt rate, helping to decrease the parameterization of the model. A snowpack temperature deficit model was also incorporated to simulate the effects of snow ripening. These two modifications provided the modelling flexibility needed to control the timing of initial snowmelt and the rate of snowmelt. Annual spring freshet lasts roughly 2 weeks in this region; however, improved model performance was observed well beyond the spring freshet period. These modifications also provided a better representation of the physical processes that delay snowmelt once the air temperature exceeds 0 °C.Key words: frozen ground, boreal forest, hydrological modelling, snow ripening, snowmelt.
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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 | 0.001 |
| 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".