Risk Assessment of Weed Resistance in the Canadian Prairies
Bibliographic record
Abstract
Agricultural practices, other than herbicide use, can affect the rate of evolution of herbicide resistance in weeds. This study examined associations of farm management practices with the occurrence of herbicide (acetyl-CoA carboxylase or acetolactate synthase inhibitor)-resistant weeds, based upon a multi-year (2001 to 2003) random survey of 370 fields/growers from the Canadian Prairies. Herbicide-resistant weeds occurred in one-quarter of the surveyed fields. The primary herbicide-resistant weed species was wild oat, with lesser occurrence of green foxtail, kochia, common chickweed, spiny sowthistle, and redroot pigweed. The risk of weed resistance was greatest in fields with cereal-based rotations and least in fields with forage crops, fallow, or where three or more crop types were grown. Weed resistance risk also was greatest in conservation-tillage systems and particularly low soil disturbance no-tillage, possibly due to greater herbicide use or weed seed bank turnover. Large farms (> 400 ha) had a greater risk of weed resistance than smaller farms, although the reason for this association was unclear. The results of this study identify cropping system diversity as the foundation of proactive weed resistance management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".