Not-so-simple patterns of neonicotinoids and diamides in small Prairie streams: implications for assessing risk and understanding pesticide dynamics
Bibliographic record
Abstract
Year-round neonicotinoids detections in waterways pose a threat to aquatic ecosystems and drinking water supplies. Neonicotinoids, and increasingly diamides, are being used in the Canadian Prairies, but there is a paucity of detection and concentration data in streams and rivers. We report on neonicotinoids and diamides in 16 streams in southern Saskatchewan, Canada between 2017 and 2019. Approximately half of all samples had measurable levels of at least one insecticide, generally below guidelines. Thiamethoxam was most frequently detected across sites (42%), followed by clothianidin (18%) and imidacloprid (9%), while diamide detections differed with location. Most samples with detections contained at least one of thiamethoxam, clothianidin, or imidacloprid (98%). About 15% of samples between 2018 and 2019 detected diamides, reflecting their increasing use in Canada. While thiamethoxam and clothianidin concentrations were similar between rain events and snowmelt, their average daily loads were greatest during snowmelt (p < 0.05); suggesting overwintering and spring freshet as a significant source to streams. Generally, agriculturally intensive subwatersheds dominated by canola and cereals had higher neonicotinoid concentrations, yet crop cover and sites explained a small proportion of the variance. Neither site, crop, flow, or year considerably accounted for the large variation in detections, suggesting a complexity of factors. Based on probability distributions, exceedances of 7% to 15% were observed for thiamethoxam, clothianidin, and imidacloprid when compared to a highly protective chronic predicted no-effect concentration guideline value, suggesting limited acute or chronic risk in these systems. The variation of insecticide concentrations reflects crop practices, precipitation, prairie hydrology, agricultural practices, and environmental conditions, and highlights the need for improved monitoring across Canada to better understand processes affecting their distribution and risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".