Winter wheat (Triticum aestivum L.) tolerance to mixtures of herbicides and fungicides applied at different timings
Bibliographic record
Abstract
Robinson, M. A., Cowbrough, M. J., Sikkema, P. H. and Tardif, F. J. 2013. Winter wheat (Triticum aestivum L.) tolerance to mixtures of herbicides and fungicides applied at different timings. Can. J. Plant Sci. 93: 491-501. Farmers commonly tank-mix herbicides and fungicides to reduce application costs. In the spring of 2008, there were reports of winter wheat injury with the application of herbicide-fungicide tank-mixes early in the growing season. This study was established to determine the tolerance of winter wheat to herbicide-fungicide mixtures as influenced by time of application. Field studies were conducted at four Ontario locations in 2009 and 2010 with three herbicides and four fungicides. Herbicide-fungicide tank-mixes were applied early, under cold conditions, and late at growth stage Zadoks 37-39. Dichlorprop/2,4-D mixed with tebuconazole caused up to 15% injury when applied early and up to 29% injury when applied late. Bromoxynil/MPCA mixed with tebuconazole injured wheat up to 15% when applied early but only 10% when applied late. Other herbicide and fungicide mixes caused a lower level of injury. Visible injury was transient and did not reduce winter wheat yields. The likelihood of tank-mixes causing injury was greater when they were applied late. The fungicide tebuconazole caused the highest level of injury when mixed with herbicides and injury was particularly high with dichlorprop/2,4-D.
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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 | 0.000 | 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".