Glyphosate-resistant giant ragweed (Ambrosia trifida L.) control with preplant herbicides in soybean [Glycine max (L.) Merr.]
Bibliographic record
Abstract
Vink, J. P., Soltani, N., Robinson, D. E., Tardif, F. J., Lawton, M. B. and Sikkema, P. H. 2012. Glyphosate-resistant giant ragweed (Ambrosia trifida L.) control with preplant herbicides in soybean [Glycine max(L.) Merr.]. Can. J. Plant Sci. 92: 913-922. Giant ragweed populations in southwestern Ontario have evolved resistance to glyphosate. Glyphosate-resistant (GR) giant ragweed interference in field crops can lead to significant yield losses. Eleven field trials [five with preplant (PP) burndown only and six with PP burndown plus residual herbicides] were conducted in 2010 and 2011 on Ontario farms with GR giant ragweed to evaluate the efficacy of various PP herbicides applied prior to soybean planting. Glyphosate applied at the recommended field dose failed to adequately control GR giant ragweed. The PP herbicides 2,4-D ester, cloransulam-methyl and saflufenacil applied alone and with glyphosate provided 97-99, 68-100 and 71-94% control, respectively and resulted in soybean yields equivalent to the weed-free check. Combinations of glyphosate plus cloransulam-methyl or linuron controlled GR giant ragweed 8 wk after application (WAA), 75-95 and 95-98%, respectively. Residual control with glyphosate plus linuron resulted in soybean yield equivalent to the weed-free check. Based on these results, GR giant ragweed can be controlled prior to soybean planting in southwestern Ontario.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".