Tolerance of Winter Wheat (<i>Triticum aestivum</i> L.) and Under Seeded Red Clover (<i>Trifolium pretense</i> L.) to Fall Applied Post-Emergent Broadleaf Herbicides
Bibliographic record
Abstract
The fall application of post-emergent (POST) herbicides on winter wheat provided effective control of emerged winter annual, biennial, and perennial broadleaf weeds. In recent years, wheat producers have seen a shift to these weeds, due in part, to the adoption of reduced-and no-tillage practices and the use of non-residual herbicides such as glyphosate in the preceding soybean and corn crops. The tolerance of winter wheat to ten herbicides, applied POST in the fall, was evaluated between 2008 and 2011 at Exeter and Ridgetown, Ontario. Winter wheat yield was not reduced by applications of MCPA ester, dicamba/ MCPA/ mecoprop, clopyralid, bromoxynil/ MCPA, thifensulfuron /tribenuron +MCPA ester, fluroxypyr +MCPA ester, and pyrasulfotole/ bromoxynil. In contrast, 2,4-D ester and dichlorprop/2,4-D, caused visible injury in June and July of the following year and consistently decreased winter wheat yield by at least 10%. Applications of 100 g a.i. ha-1 saflufenacil also decreased winter wheat yield in two of the four harvest years examined. None of the herbicide options examined were safe on red clover when it was under seeded the spring following winter wheat planting. All herbicides significantly decreased red clover dry biomass one month after wheat harvest.
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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) | 0.000 | 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 | 0.000 | 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".