Potential spring canola yield losses due to weeds in Canada and the United States
Bibliographic record
Abstract
Abstract Weeds represent one of the most important biotic threats to agricultural plant health, and the potential global impact of weeds on crop yields is similar to that of all other pests (animal pests and pathogens) combined. Canola is the most-grown crop in Canada based on seeded area and generates on average Can$29.9 billion in economic activity each year. The objective of this report, sponsored by the Weed Science Society of America Weed Loss Committee, was to provide an updated estimate of potential yield and monetary losses due to weed interference in spring canola grown in Canada and the United States. Quantitative yield data from field experiments were provided by researchers and weed science professionals in the northern Great Plains region; the major canola-producing area of North America. Overall, 89 yield loss estimates were compiled, covering the 18-yr period from 2003 to 2020. Average canola yield losses due to weed interference in Alberta, Saskatchewan, Manitoba, and North Dakota were 35%, 30%, 18%, and 28%, respectively. Potential yield losses weighted by canola harvested area averaged 30%, 28%, and 30% for Canada, the United States, and both countries combined, respectively. Therefore, unfettered weed interference in spring canola represents a potential monetary loss of Can$2.21 billion, $0.16 billion, and $2.37 billion for farmers in Canada, the United States, and both countries combined. The realization of such losses could manifest through continued selection for herbicide-resistant weeds, indicating the critical need for canola farmers to diversify resistance selection pressures by implementing proactive integrated weed management programs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| 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".