Control of glyphosate-resistant Canada fleabane [Conyza canadensis (L.) Cronq.] with preplant herbicide tankmixes in soybean [Glycine max. (L). Merr.]
Bibliographic record
Abstract
Byker, H. P., Soltani, N., Robinson, D. E., Tardif, F. J., Lawton, M. B. and Sikkema, P. H. 2013. Control of glyphosate-resistant Canada fleabane [ Conyza canadensis (L.) Cronq.] with preplant herbicide tankmixes in soybean [ Glycine max . (L). Merr.]. Can. J. Plant Sci. 93: 659-667. Glyphosate previously provided excellent control of Canada fleabane; however, with the evolution of glyphosate-resistant (GR) Canada fleabane in Ontario, alternative herbicides must be identified for control of this weed in soybean. The objective of this study was to identify preplant herbicide tankmixes that provide effective control of GR Canada fleabane. A total of 12 field trials were completed over a 2-yr period (2011, 2012) in fields previously confirmed with GR Canada fleabane. Preplant tankmixes of glyphosate (900 g a.e. ha-1) plus saflufenacil (25 g a.i. ha-1) or saflufenacil/dimethenamid-p (245 g a.i. ha-1) provided greater than 87% control 4 wk after application (WAA) across all sites. Glyphosate (900 g a.e. ha-1) tankmixed with metribuzin (1120 g a.i. ha-1), cloransulam-methyl (35 g a.i. ha-1) or flumetsulam (70 g a.i. ha-1) provided 78 to 99% control 8 WAA. Control of GR Canada fleabane prior to soybean emergence is essential as currently there are no herbicides that provide acceptable control in-crop. Because of the lack of in-crop options, spring residual herbicides may be required for season long control in regions where Canada fleabane emerges throughout the year to reduce Canada fleabane seed production and populations in subsequent years.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".