Environmental Impact of Glyphosate-Resistant Weeds in Canada
Bibliographic record
Abstract
Glyphosate-resistant (GR) giant ragweed, horseweed, and common ragweed were confirmed in southwestern Ontario, Canada in 2008, 2010, and 2011, respectively. In the western prairie provinces of Alberta and Saskatchewan, GR (plus acetolactate synthase inhibitor-resistant) kochia was discovered in 2011. This symposium paper estimates the environmental impact (EI) of the top herbicide treatments or programs used to manage these GR weed species in the major field crops grown in each region. For each herbicide treatment, EI (per ha basis) was calculated as the environmental impact quotient (EIQ), which quantifies the relative potential risk of pesticide active ingredients on human and ecological health based on risk components to farm workers, consumers, and the environment, multiplied by the application rate (kg ai ha−1). Total EI is defined as EI (per ha basis) multiplied by the application area (i.e., land area affected by a GR weed). It was assumed that all herbicide treatments would supplement the continued usage of glyphosate because of its broad spectrum weed control. For the control of these GR weeds, most treatments contain auxinic or protoporphyrinogen oxidase (PPO)-inhibiting herbicides. The majority of auxinic herbicide treatments result in low (EI ≤ 10) to moderate (11 to 20) EI, whereas all treatments of PPO inhibitors have low EI. Total EI of GR horseweed and kochia will generally be greater than that of giant or common ragweed because of rapid seed dispersal. For recommended herbicide treatments to control GR weeds (and herbicide-resistant weeds in general), EI data should be routinely included with cost and site of action in weed control extension publications and software, so that growers have the information needed to assess the EI of their actions.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".