Comparison of weed control strategies in glyphosateresistant soybean [<i>Glycine max</i> (L.) Merr.] in Atlantic Canada
Bibliographic record
Abstract
Glyphosate-resistant soybean cultivars with low corn heat unit requirements have only recently become available in the Atlantic region of Canada. In a field experiment near Charlottetown, Prince Edward Island, in 1998 and 1999, we evaluated the effect of time of application of glyphosate and application of selected herbicides, flumetsulam, metolachlor, imazethapyr, metribuzin, and bentazon on weed control and tolerance and yield of three glyphosate-resistant soybean cultivars having different maturity ratings. Glyphosate was applied at 0.90 kg ai ha-1 at the first, second or third trifoliate leaf stage, and sequentially at the first and third or second and third trifoliate stages. The sequential treatments of glyphosate resulted in 89 to 100% weed control, and yields achieved with glyphosate applied at the second or third trifoliate stages did not usually differ from sequential treatments. The other selected herbicides did not injure the cultivars and within the group, all treatments resulted in comparable yields. Species that emerge in a single early-season flush were controlled at 6 wk after treatment with glyphosate applied as a single application, but not on a species such as wild radish that have later flushes or emerge throughout the season. Glyphosate applied as a single application at the late second to third trifoliate stage can provide optimum weed control and crop yield with lower herbicide cost than an alternative herbicide treatment. Key words: Herbicide timing, narrow-row soybean, soybean cultivars, 2601R, 2701R, S14-M7
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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 | 0.000 | 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 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".