Evaluation of WARMF model for flow and nitrogen transport in an agricultural watershed under a cold climate
Bibliographic record
Abstract
The Watershed Analysis Risk Management Framework (WARMF) model is adapted to simulate flow and nitrate-N transport in an agricultural watershed in Quebec, Canada. The model was evaluated for the St. Esprit Watershed (24.3 km2), which is a part of the 210 km2 St. Esprit river basin, a tributary of the L'Assomption Watershed (4,220 km2). WARMF's hydrologic calibration and validation was performed using data from the gauge station located at the outlet of the watershed. Water-quality data collected were used to guide water quality calibration/validation. Simulations were carried out from 1994 to 1996; data from 1994 and 1995 were used for model calibration and data from 1996 were used for model validation. The model performed reasonably well in simulating the hydrologic response and nitrate losses at the outlet of the watershed. The R2 between the observed and simulated monthly stream flow for calibration was 0.92, and that for validation was 0.94. The corresponding coefficients of efficiency (E) were 0.89 and 0.91. The R2 and E values for calibration/validation of NO3−-N loads simulation were 0.89/0.84 and 0.86/0.75, respectively. Thus, the model simulated monthly flow and nitrogen losses with a good degree of accuracy over the entire year.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".