Effect of soybean and winter wheat herbicides on oilseed radish establishment and growth
Bibliographic record
Abstract
Residual herbicides can cause injury to a future crop. Residual herbicides applied to soybean [Glycine max (L.) Merr.] or winter wheat (Triticum aestivum L.) may impact cover crops seeded in the same growing season. The objective of this study was to determine the effect of residual herbicides on oilseed radish (Raphanus sativus L.) establishment and growth after 10 postemergence (POST) broadleaf herbicides were applied in winter wheat and 12 preemergence (PRE) and 7 POST herbicides were applied in soybean. Oilseed radish injury was assessed 14 and 28 d after emergence (DAE) and stand density and biomass were measured 28 DAE. Residual herbicides used for broadleaf weed control in winter wheat caused less than 5% oilseed radish visible injury and there was no reduction in stand density and biomass. In soybean, imazethapyr applied PRE caused 43% and 48% oilseed radish injury at 14 and 28 DAE, respectively. There was no decrease in oilseed radish stand density and biomass. Imazethapyr applied POST to soybean caused 47% and 59% oilseed radish injury at 14 and 28 DAE, respectively, and decreased oilseed radish biomass by 65%. There was no decrease in oilseed radish stand density. The results from this study conclude that many of the herbicides commonly used in winter wheat and soybean in Ontario do not negatively impact oilseed radish establishment and growth.
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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".