Selecting a Pesticide Fate Model at the Watershed Scale Using a Multi-criteria Analysis
Bibliographic record
Abstract
Abstract Numerous models have been developed over the last decades to simulate the fate of pesticides at the watershed scale. Based on a literature review, we inventoried thirty-six models categorized as management, research, screening or multimedia models, each of them having specific strengths and weaknesses. Given this large number of models, it may be difficult for potential users (stakeholders or scientists) to find the most suited one with respect to their needs. To help in this process, this paper proposes a pragmatic approach based on a multi-criteria analysis. Selection criteria are defined following the user's needs and classified in five classes: modelling characteristics, output variables, model applicability, possibilities to simulate best management practices (BMPs) and ease of use. The relative importance of each criterion is quantified by a weight and the total score of a model is calculated by adding the resulting weights of satisfied criteria. This selection framework is illustrated with a case study that consists in selecting a model to develop water quality standards at the watershed scale with respect to the implementation of BMPs. This resulted in the selection of three models: BASINS, SWAT and GIBSI.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".