From peaks to prairies: a time-of-travel synoptic survey of pesticides in watersheds of southern Alberta, Canada
Bibliographic record
Abstract
A time-of-travel synoptic survey was conducted in August 2014 to assess pesticide occurrence in major river subbasins of southern Alberta, Canada. We collected 155 water samples from Alberta's South Saskatchewan River Basin (Bow, Oldman, Red Deer, and South Saskatchewan river subbasins) and analysed them for 106 pesticides. Samples were collected from mainstems (rivers) as well as tributaries and effluents (point sources) discharging into the rivers in an estimated time-of-travel to capture agricultural and urban contributions as the water flowed eastward from headwaters in the Rocky Mountains. The aim of the study was to assess point-source contributions of pesticides to Alberta's South Saskatchewan River Basin mainstems during low flow conditions. In addition to examining differences in pesticide contributions among source type and subbasins, the cumulative effect of inputs on the mainstem rivers, the influence of land use, and seasonality of pesticides in select effluents was analysed. We detected 17 pesticides and 1 metabolite, reflecting runoff from agricultural and urban land use. Pesticide detection frequency was significantly higher in effluents than in tributaries and mainstems, and pesticides were detected in effluents in summer and winter. Our evaluation of land-use relationships highlighted the influence of agriculture on pesticide occurrence in these aquatic agroecosystems. To our knowledge, this is the first watershed-wide time-of-travel survey of pesticides conducted in southern Alberta to measure contributions of pesticide loads from effluents and tributaries to the mainstems in all regions of Alberta's South Saskatchewan River Basin.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".