Using the Pesticide Toxicity Index to show the potential ecosystem benefits of on‐farm biobeds
Bibliographic record
Abstract
The influent and effluent of two single-cell biobeds (Province of Alberta, Canada) and two dual cell-biobeds (Province of Saskatchewan, Canada) were monitored during a number of growing seasons. A total of 59 unique pesticide active ingredients were detected, with all biobed influent samples (n = 54) and 93% of effluent samples (n = 54) containing pesticide mixtures. About one-half of the effluent samples in both single-cell (56%) and dual-cell (45%) biobeds contained active ingredients that have Groundwater Ubiquity Score (GUS) values >2.8 and so were more likely to move through the biomatrix materials into effluent. The Pesticide Toxicity Index (PTI) calculated for aquatic indicator species (i.e., vascular and nonvascular plants, invertebrates, and fish) was always larger for influent samples (e.g., median PTI >500 for invertebrates in dual-cell biobed) than effluent samples (i.e., median PTI <1). As such, this study demonstrates the potential ecosystem benefits of the broad adoption of on-farm biobeds in the Canadian Prairies for recycling tank rinsate as a strategy to accelerate a green economy. Although biobeds were highly effective in reducing the concentrations for pesticides with a wide range of soil organic carbon coefficient and half-life values, the biobed effectiveness was relatively poor for the herbicides clopyralid, diclofop, fluroxypyr, and imazethapyr. Clopyralid (3.02), fluroxypyr (3.70), and imazethapyr (3.90) all have relatively high GUS values (>2.8) and are thus more likely to be detected in effluent than active ingredients with smaller GUS values. This suggests that further improvements in biosystem design need to be made for optimizing the recycling of these pesticides.
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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 | 0.000 | 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".