Management of agricultural pesticide rinsate using a biobed under Manitoba conditions
Bibliographic record
Abstract
A dual-celled biobed was built at the Ian Morrison N. Research farm in Carman, MB to degrade and retain the pesticides from pesticide rinsate derived at the farm. The biobed was designed for Manitoba conditions with considerations taken from existing biobeds in the Province of Saskatchewan. Temperature and moisture of the biomixture contained within the biobed was monitored using probes for the duration of the study. Three pesticides (2,4-DB, dicamba, and metrafenone) were detected in the biomixture prior to pesticide input from rinsing in 2020 but had degraded by the end of the season and were no longer detected. The Influent of the biobed was analyzed for a suite of pesticides and 23 pesticides and one pesticide metabolite were detected. In Effluent One only 11 of the 24 compounds were detected, and six compounds were detected in Effluent Two. Total pesticide concentrations significantly decreased for Influent > Effluent One > Effluent Two. Pesticides detected in the effluents tended to have higher GUS or Koc values. In 2021, a large influx of prepared spraying liquid containing glufosinate (approximately 680L) overburdened the biobed and resulted in reduced degradation and increasing mean concentrations from Influent > Effluent One > Effluent Two. Glufosinate and seven other pesticides detected in the influent were not measured in biomix due to method limitations. There were three pesticides (bifenthrin, trifloxystrobin, and diuron) detected in the biomixture samples in 2021, and only diuron, which had the highest GUS and Koc values, was still detected at the end of the season.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".