Herbicides in Alberta Rainfall as Affected by
Bibliographic record
Abstract
A study of herbicides in Alberta rainfall was conducted at 17 locations in 1999 and 18 locations in 2000. Rainfall samples (bulk deposition) were collected using a funnel and glass bottle at weekly to biweekly intervals from April to September. Samples were analyzed for 13 herbicides in 1999 and 19 herbicides in 2000 using a MSD-GC method. Herbicides were detected in the rainfall on most sample dates at every location. The herbicides detected in the highest amounts (in order) were: 2,4-D, MCPA, bromoxynil, dicamba and mecoprop. Detections were quite consistent between years. Herbicide amounts over the season were lowest at the remote locations (21–96 µg/m2 2,4-D), intermediate in the City of Lethbridge (82–109 µg/m2 2,4-D) and highest in the farming areas (44–315 µg/m2 2,4-D). The highest herbicide concentrations (17–53 µg/L 2,4-D) occurred when there was a small rainfall (0.1–0.2 mm) during the agricultural spraying season. Herbicide concentrations occasionally exceeded the Canadian Drinking Water Guidelines and frequently exceeded the Aquatic Life Guidelines. In general, herbicide levels in rainfall reflected agricultural use (sales) patterns. The highest 2,4-D detections occurred in southern Alberta, the highest MCPA detections were in central Alberta. In a side-by-side test, our funnel method of collecting rainfall yielded herbicide amounts that were nine times higher than a wet-only automated MIC sampler. Most previous Canadian studies used the MIC sampler and therefore may have underestimated levels of herbicides in rainfall. A more expansive study is needed to determine the lev-els of herbicides in rainfall across the Canadian Prairies.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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".