The interaction of annual weed and white mold management systems for dry bean production in Canada
Bibliographic record
Abstract
Pynenburg, G. M., Sikkema, P. H., Robinson, D. E. and Gillard, C. L. 2011. The interaction of annual weed and white mold management systems for dry bean production in Canada. Can. J. Plant Sci. 91: 587-598. Annual weeds and white mold (Sclerotinia sclerotiorum) are serious pests in dry bean, and can cause substantial yield losses. The proper management of these pests is essential for profitable production. A 2-yr study conducted at three field locations in Ontario examined the effects of two pre-plant incorporated herbicide programs on weed management and three foliar fungicides on white mold development. In addition, thiamethoxam was evaluated for its ability to alleviate stress caused by annual weeds and white mold. Interactions among disease severity, weed control, agronomics and economic returns were examined. Thiamethoxam seed treatment had inconsistent benefits with respect to plant emergence and vigour, harvested weight, seed weight and economic returns. There were no benefits for the other parameters measured. The premium herbicide program (s-metolachlor plus imazethapyr) reduced weed ground cover, white mold severity and pod drop, and increased 100-seed weight, harvested weight and net economic return compared with the economic herbicide program (trifluralin). The foliar fungicides reduced white mold severity and pod drop, while increasing 100-seed weight, harvested yield and net economic return. Fluazinam resulted in the lowest white mold severity, and the highest yield and treatment return, when compared with cyprodinil/fludioxonil and boscalid in some environments. High weed pressure in the presence of white mold increased disease severity. Where treatment differences occurred, the premium herbicide program and fluazinam foliar fungicide resulted in the highest net economic return to growers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".