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 SWAT BF , was applied to the Willow Creek watershed (15.1 km 2 ) 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 SWAT BF 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. SWAT BF 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 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 | 0.000 | 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".