Weed management in cranberry bean with linuron
Bibliographic record
Abstract
Soltani, N., Nurse, R. E., Shropshire, C. and Sikkema, P. H. 2011. Weed management in cranberry bean with linuron. Can. J. Plant Sci. 91: 881–888. Field studies were conducted at the Huron Research Station near Exeter, Ontario, in 2006 to 2009 to determine if the sequential application of trifluralin plus imazethapyr applied preplant incorporated (PPI) followed by linuron applied pre-emergence (PRE) at various doses can be used as an effective weed management strategy in cranberry bean production. There was minimal crop injury (6% or less) with various herbicides evaluated at 1 and 4 wk after emergence (WAE). Trifluralin plus imazethapyr applied PPI provided 97 to 100% control of common lambsquarters, 100% control of redroot pigweed, 99 to 100% control of wild mustard, 93 to 100% control of common ragweed, and 97 to 100% control of green foxtail. Linuron applied PRE provided 11 to 100% control of common lambsquarters, 90 to 100% control of redroot pigweed, 78 to 100% control of wild mustard, 71 to 100% control of common ragweed, and 20 to 100% control of green foxtail. The sequential application of trifluralin plus imazethapyr applied PPI followed by linuron applied PRE at various doses provided 100% control of common lambsquarters, 100% control of redroot pigweed, 100% control of wild mustard, 96 to 100% control of common ragweed, and 97 to 100% control of green foxtail. Weed density and shoot dry weight correlated well with the level of weed control. All of the herbicide treatments evaluated increased cranberry bean yield compared with the weedy control. Based on these results the sequential application of imazethapyr plus trifluralin applied PPI followed by linuron applied PRE at 1000 and 2500 g a.i. ha −1 provides a safe and efficacious weed management strategy in cranberry bean production.
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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".