Herbicide Rate Effects on Weed and Root Maggot Dynamics in Canola
Bibliographic record
Abstract
Weed management strategies can influence insect infestations in field crops, yet no attempts have been made previously to manipulate weed populations in canola for integrated weed and insect management. Field studies were conducted during 2003 to 2005 at Lacombe and Beaverlodge, Alberta, Canada to manipulate weed and root maggot, Delia spp. (Diptera: Anthomyiidae), interactions in canola. Densities of monocot weeds were varied by altering herbicide applications, with rates ranging from 0 to 100% of the rate recommended. Weed populations declined, and yields were variable with increased herbicide rates. Root maggot damage decreased with increases in monocot weed dry weight for both canola species at both study sites. Results support the hypothesis that heterogenous environments, arising from mixed populations of monocot weeds with canola, minimize opportunities for females of Delia spp. to complete the behavioral sequence required for oviposition, leading to reduced infestation levels in weedy systems. However, effects of dicot weeds on root maggot infestations varied between sites as a result of site-related differences in weed species complexes. When wild mustard was common, crop damage increased, because this weed can serve as an alternate host for root maggots. The study emphasizes the importance of adopting crop management practices that are compatible for both weed and root maggot control.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".