Glyphosate-resistant Canada fleabane [Conyza canadensis (L). Cronq.]: Dose response to glyphosate and control with postemergence herbicides in soybean in Ontario
Bibliographic record
Abstract
Byker, H. P., Soltani, N., Robinson, D. E., Tardif, F. J., Lawton, M. B. and Sikkema, P. H. 2013. Glyphosate-resistant Canada fleabane [ Conyza canadensis (L). Cronq.]: Dose response to glyphosate and control with postemergence herbicides in soybean in Ontario. Can. J. Plant Sci. 93: 1187-1193. Glyphosate-resistant (GR) Canada fleabane was first reported in Ontario in 2010. Twelve field studies were conducted over a 2-yr period (2011, 2012) to determine the biologically effective dose of glyphosate in the field and to determine the efficacy of postemergence herbicides for control of GR Canada fleabane. A dose between 1271 and 5652 g a.e. ha-1 was required for a 50% reduction in Canada fleabane biomass depending on site and year. To achieve acceptable (>95%) control with glyphosate, 18 840 to 43 200 g a.e. ha-1 of glyphosate was required across all sites. Therefore application of glyphosate at these doses for acceptable (>95%) control is not economical. None of the postemergence herbicides labeled for use in soybean provided acceptable (>95%) control, with cloransulam providing the greatest control (67%) 4 wk after application. Therefore, GR Canada fleabane must be controlled prior to soybean emergence as current herbicides registered in soybean for postemergence use do not provide sufficient control to prevent soybean yield loss.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".