Water Quality Modeling of Two Agricultural Fields in Southern Quebec Using SWAT
Bibliographic record
Abstract
To study the dynamics of nutrient transport at the field scale, we collected data from two tile-drained agricultural fields in the Pike River watershed of southern Quebec. A two-year data set was used to calibrate and validate the Soil and Water Assessment Tool (SWAT) for sediment, nitrate, and phosphorus loads exiting the field through surface runoff and tile drainage. We found that SWAT output on water quality required an accurate estimation of the timing and form of field management practices employed. After calibration, the monthly coefficients of performance (Cp) over four site-years varied from 0.23 to 0.89 for sediment loads, from 0.48 to 1.35 for nitrate loads, and from 0.38 to 0.67 for total phosphorus loads. Subsurface nitrate loads accounted for 97.7% and 86.7% of the total nitrate yield, while particulate phosphorus accounted for 61.2% and 87.7% of total phosphorus load on sites 1 and 2, respectively. SWAT underestimated nitrate loads in subsurface drainage during spring snowmelt and large storms. Sediments and particulate phosphorus predictions were most accurate of all simulated parameters, whereas dissolved phosphorus was marginally overestimated year-round. Overall, SWAT satisfactorily reproduced field observations for sediment and nutrient transport and could be used to compare the impacts of implementing different best management practices (BMP) on individual fields for the study site.
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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 | 0.000 |
| 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".