Multicriteria Review of Nonpoint Source Water Quality Models for Nutrients, Sediments, and Pathogens
Bibliographic record
Abstract
Abstract There now exists a large number of nonpoint source models that have been developed for watershed management issues. As part of the National Agri-Environmental Standards Initiative (NAESI), it was necessary to evaluate which model or models would be the best choice for examining various best management practices for nutrients, sediments, and pathogens in test watersheds. Environment Canada has committed to the development of environmental performance standards that will guide environmentally sustainable agricultural practices and management. A literature review was carried out and a short list of 13 models was selected that met the basic needs of the project. These models were subsequently evaluated based on a multicriteria analysis that considered 21 model characteristics. A weight and a total score quantify the relative importance of each criterion. The top six models were SWAT (Soil and Water Assessment Tool), AnnAGNPS (Annualized Agricultural Nonpoint Source), BASINS (Better Assessment Science Integrating Point and Nonpoint Sources), GIBSI (Gestion Intégrée des Bassins versants á l'aide d'un Systéme Informatisé), AGNPS (Agricultural Nonpoint Source), and HSPF (Hydrologic Simulation Program - Fortran).
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 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".