Modification of SWAT for modelling streamflow from forested watersheds on the Canadian Boreal Plain
Bibliographic record
Abstract
Several modifications were made to the Soil and Water Assessment Tool (SWAT) to better represent processes occurring within forested watersheds on the Boreal Plain in Canada. The modified model, called SWATBF, was applied to the Willow Creek watershed (15.1 km2) in north central Alberta. The performance of the model for the calibration period (2001–2003) was good with coefficients of efficiency of 0.89 and 0.81 being achieved for the prediction of monthly and daily runoff, respectively. However, it was found that SWATBF did not perform as well for the validation period (2004–2006) with the monthly and daily coefficients of efficiency being 0.44 and 0.27, respectively. Potential sources of error to explain the decline in model performance for the validation period are discussed. SWATBF has the potential to be used as a tool by forest managers for predicting the effects of land use change on the Boreal Plain provided that it can be satisfactorily validated.
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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) | 0.001 | 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 | 0.001 |
| 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".