Pesticide presence in stream water, suspended sediment and biofilm is strongly linked to upstream catchment land use and crop type
Bibliographic record
Abstract
Pesticide pollution can present high ecological risks to aquatic ecosystems. Small streams are particularly susceptible. There is a need for reproducible and readily available methods to identify aquatic regions at risk of pesticide contamination. There is currently a limited understanding of the relationship between upstream catchment land use and the presence of pesticides in multiple aquatic matrices. The aim of this study was to develop empirical relationships between different land uses and the levels of pesticides detected in multiple aquatic matrices. The inclusion of biofilm and suspended sediment as monitoring matrices has recently been proven effective for the characterization of pesticide exposure in stream ecosystems. Ten streams in Ontario, Canada with a variety of upstream catchment land uses were sampled in 2021 and 2022. Water, suspended sediment and biofilm were collected and analyzed from each site for the presence of approximately 500 different pesticides. Each of the three matrices exhibited distinctive pesticide exposure profiles. We found a significant relationship between the percentage of agriculture and urban land use and the detection of multiple pesticides in water, sediment and biofilm (logistic regressions, P<0.05). Statistically significant probabilistic models capable of predicting pesticide detections based on upstream catchment land use were developed. High-resolution cover crop maps identified soybeans, corn and other agriculture (e.g., vegetables, berries, canola) as the key variables associated with individual pesticide detection frequencies in each of the three matrices (linear regressions, P<0.05). Soybean land use was also the strongest predictor of site-wide pesticide pollution. This modelling approach using upstream catchment land use variables has the potential to be a powerful tool to identify streams at risk of pesticide pollution.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".