First report of glyphosate-resistant downy brome (Bromus tectorum L.) in Canada
Bibliographic record
Abstract
Abstract Glyphosate is the most used herbicide worldwide, and is an important source of economical weed control in glyphosate-resistant crops, and conservation tillage systems, among other uses. Downy brome (Bromus tectorum L.), otherwise known as cheatgrass, is a highly invasive winter-annual grass weed in cropping systems, pastureland, and naturalized or ruderal areas in western North America. In 2021, a downy brome population remained uncontrolled following four applications of glyphosate in a glyphosate-resistant canola (Brassica napus L.) field located in Taber County, Alberta, Canada. All individuals from the subsequent generation of the population survived glyphosate treatment at the typical field rate (900 g ae ha−1). In dose-response bioassays, the putative glyphosate-resistant population exhibited 10.6- to 11.9-fold, 7.7- to 8.7-fold, 7.8- to 8.8-fold, and 8.3- to 9.5-fold resistance to glyphosate based on plant survival, visible control, and biomass fresh weight and dry weight, respectively, compared with two susceptible populations 21 days after treatment. Estimated glyphosate rates for 80% control of this population ranged from 2795 to 4511 g ae ha−1; well above common usage rates. This downy brome population represents the first glyphosate-resistant grass weed confirmed in Canada, and the second weed species exhibiting glyphosate resistance in the Canadian prairie region.
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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 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".