Auxin Herbicides and Pesticide Mixtures in Groundwater of a Canadian Prairie Province
Bibliographic record
Abstract
Groundwater samples were collected from piezometers and water table wells in both dryland and irrigated agricultural regions of Alberta, Canada, to examine the occurrence of pesticide mixtures. Fourteen current‐use pesticides and two historical compounds were detected over a 3‐yr sampling period. Pesticide mixtures were detected in ∼3% of the groundwater samples, and the frequency of detection increased from spring (1.5%) to summer (3.8%) and fall (4.8%). Pesticide mixtures always consisted of at least one of two auxin herbicides: 2,4‐dichlorophenoxyacetic acid (2,4‐D) or 2‐methyl‐4‐chlorophenoxyacetic acid (MCPA). 19% of all samples contained a single pesticide, with auxin herbicides 2,4‐D (7.3%), MCPA (4.4%), and clopyralid (3.9%) being most prevalent. We detected 2,4‐D predominantly in the fall (72% of 2,4‐D detections) and less in spring and summer (28%). We detected MCPA mostly in summer (85% of MCPA detections) and less in spring and fall (15%). Clopyralid was more evenly detected across spring (30%), summer (25%), and fall (45%). Since the auxin herbicides above are typically applied in summer, results suggest that each herbicide may have different mobility and persistence characteristics in prairie soils. Guidelines for Canadian Drinking Water Quality have been set for a range of individual pesticides, but not for pesticide mixtures. If Canada is to establish such guidelines, this study demonstrates that auxin herbicides should be prioritized. In addition, only 7 of the 16 compounds detected in this study have established maximum acceptable concentrations (MACs), excluding clopyralid, which was detected in all three sampling years. Core Ideas Sixteen pesticides were detected in groundwater, most frequently auxin herbicides. Detection of pesticide mixtures increased from spring (1.5%) to fall (4.8%). Samples with pesticide mixtures always contained at least one auxin herbicide. In establishing water quality guidelines for mixtures, Canada must prioritize auxins.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".