Risk Assessment of Glyphosate Resistance in Western Canada
Bibliographic record
Abstract
With increasing incidence of glyphosate-resistant weeds worldwide, greater farmer awareness of the importance of glyphosate stewardship and proactive glyphosate-resistance management is needed. A Web-based decision-support tool ( http://www.weedtool.com ) comprising 10 questions has been developed primarily for farmers in western Canada to assess the relative risk of selection for glyphosate-resistant weeds on a field-by-field basis. We describe the rationale for the questions and how a response to a particular question influences the risk rating. Practices with the greatest risk weighting in western Canadian cropping systems are lack of crop-rotation diversity (growing mainly oilseeds) and a high frequency of glyphosate-resistant crops in the rotation. Three case scenarios are outlined—low, moderate, and high risk of glyphosate-resistance evolution. Based on the overall risk rating, three best-management practices are recommended to reduce the risk of glyphosate resistance in weeds.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".