Long‐Term Ground Water Quality Impacts from the Use of Hexazinone for the Commercial Production of Lowbush Blueberries
Bibliographic record
Abstract
Abstract Lowbush blueberries, native to eastern Canada and Maine, are an important economic crop in these areas. Herbicides containing the active ingredient hexazinone are commonly applied to blueberry fields, and there is a high frequency of detection of relatively low concentrations of hexazinone in domestic wells located close to areas of lowbush blueberry production. The objective of this study was to determine the long‐term impacts from hexazinone‐based herbicide use on ground water quality in the immediate growing areas. Physical and chemical hydrogeologic data were collected for an outwash sand and gravel aquifer in southwestern New Brunswick, Canada. The majority of the land overlying the aquifer is devoted to lowbush blueberry production. Twenty‐one nested monitoring wells were sampled for hexazinone and hexazinone metabolites over a four‐year period. Hexazinone was consistently detected at values of 1 to 8 parts per billion (ppb) in all but two of these wells, one that is upgradient of herbicide applications, and one that is downgradient with anoxic conditions. Hexazinone metabolites B and A1 were also detected in all but two of the 21 wells at values ranging from 0.5 to 2.5 ppb. The hexazinone and metabolite data suggest both aerobic and anaerobic degradation of hexazinone. Complete degradation of hexazinone appears to occur only in the one downgradient well exhibiting anoxic ground water conditions. Concentrations of hexazinone and its metabolites in the ground water were essentially constant over the four‐year period.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".