Comparing winter-time herbicide behavior and exports in urban, rural, and mixed-use watersheds
Bibliographic record
Abstract
The presence of pesticides in streams in winter, five to six years following bans on their municipal use suggests that complicated transport behaviour, such as subsurface retention and/or accumulation of pesticides and its release during storms, could be important for understanding recovery time frames following bans or legislation that aim to reduce chemical inputs. We investigated late fall and winter dynamics of four herbicides in paired urban and rural watersheds in Toronto, Canada during rainfall and snowmelt. The range of average concentrations and loads of the sum of atrazine, metolachlor, 2,4-D and mecoprop overlapped in the two types of watersheds, with slightly higher average concentrations in the rural watershed. Relatively consistent herbicide concentration-discharge patterns (i.e. dilution) were observed in the urban sub-watersheds during rainfall, while concentration-discharge patterns were much more variable in the rural watershed. This suggests relatively uniform transport pathways across the urban sub-watersheds, compared to the rural watershed. Concentration-discharge patterns of the neutral herbicides atrazine and metolachlor were similar in both watersheds during snowmelt, though varying discharge patterns resulted in divergent timings of peak concentrations. In contrast, the acidic pesticides 2,4-D and mecoprop, which are primarily associated with urban uses, showed much more variable behavior across both watersheds and merit further investigation. Overall, this work highlights the need to consider pesticide dynamics throughout the year in order to more thoroughly assess the long-term efficacy of legislation governing their use.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".