Modeling the effects of agricultural BMPs on sediments, nutrients, and water quality of the Beaurivage River watershed (Quebec, Canada)
Bibliographic record
Abstract
Agriculture has evolved into the largest non-point source of surface water pollution in Canada as a result of intensification over the past forty years. The Canadian WEBs project (Watershed Evaluation of Beneficial Management Practices, BMPs) was launched to evaluate the environmental and economic performance of BMPs as a means to mitigate agricultural sediment and nutrient issues. In this paper, the Gestion Intégrée des Bassins versant à l’aide d’un Système Informatisé (GIBSI) (or Integrated Watershed Management using a Computer System) integrated modeling framework was used to evaluate the effects of different BMPs on sediment and nutrient yields, as well as water quality in the Beaurivage River watershed in the province of Quebec. A reference scenario was developed that describes the current situation (i.e., base case scenario) of the watershed by calibrating the models used within GIBSI, namely HYDROTEL for hydrology, the Revised Universal Soil Loss Equation (RUSLE) for soil erosion, the Erosion-the Productivity Impact Calculator (EPIC) of the Soil and Water Assessment Tool (SWAT) for contaminant transport and fate, and QUAL2E for stream water quality. The effects of four BMPs were studied: (1) vegetated riparian buffer strips, (2) precision slurry application, (3) grassland conversion of cereal and corn fields, and (4) no-till (on corn fields). Simulation results indicate that BMPs can be effective in reducing nutrient and suspended sediment loads in both surface runoff and stream flow. More specifically, buffer strip and crop rotation showed better efficiency than hog-slurry management and no-till on corn BMPs. Moreover, results highlight the need for further investigation of sediment dynamics in the stream network as well as in the riparian buffer strips.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".