Co-application of thifensulfuron with glyphosate accentuates soybean injury
Bibliographic record
Abstract
Three trials (two in 2019 and one in 2020) were completed at the University of Guelph, Huron Research Station near Exeter, ON, to determine if the co-application of thifensulfuron with glyphosate accentuates soybean injury in glyphosate-resistant (GR) soybean. At 1, 2, 4, and 8 weeks after treatment (WAT), thifensulfuron (6 and 12 g a.i.·ha−1 representing the 1× and 2× rate, respectively) applied POST with no adjuvants caused up to 5% soybean injury. The addition of a non-ionic surfactant + UAN to thifensulfuron increased soybean injury to up to 24%. There was no decrease in soybean density, dry biomass, height, and yield, except soybean dry biomass was reduced up to 22% with the addition of adjuvants to thifensulfuron at the 2× rate. Glyphosate (1800 and 3600 g·ha−1 representing the 1× and 2× rate, respectively) applied POST caused no adverse effect on soybean injury parameters evaluated. The co-application of glyphosate + thifensulfuron at the 1× and 2× rates, without additional adjuvants, caused a synergistic increase in soybean injury at 1, 2, 4, and 8 WAT, and a synergistic decrease in dry biomass and height. All other interactions were additive. The co-application of glyphosate + thifensulfuron at the 1× and 2× rates, with additional adjuvants, produced a synergistic increase in injury at 1 (1× and 2× rate), 4 (1× rate), and 8 (1× rate) WAT in soybean. All other interactions were additive.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".