Influence of a Range of Dosages of MCPA, Glyphosate, and Thifensulfuron: Tribenuron (2:1) on Conventional Canola (<i>Brassica napus</i>) and White Bean (<i>Phaseolus vulgaris</i>) Growth and Yield
Bibliographic record
Abstract
There is a high potential for inadvertent herbicide injury to crops in western Canada on an annual basis because of the diversity of crops grown in close proximity to each other, although accurate data regarding the annual number of injury incidents is not available. A field study was conducted at two locations in southern Manitoba, Canada, in 2001 and 2002, to investigate the effects of a range of dosages of MCPA ester, glyphosate, and thifensulfuron:tribenuron (2:1) applied to the seedling growth stage of conventional (nongenetically engineered) canola and white bean on subsequent shoot dry matter and crop yield. Similar to other studies that utilized sublethal herbicide dosages, results between site-years were variable, particularly for crop yield. Where possible, a nonlinear log-logistic model was fitted to the data. Generally, canola was more sensitive than white bean to the herbicides used in this study. Based on the fitted regression equations and recorded mean values for canola, 10% of the commercial herbicide dosage normally applied in other (possibly adjacent) crops caused greater than 10% canola yield loss for 9 of 12 unique combinations of herbicide-site-year. For white bean, 10% of the commercial herbicide dosage caused yield losses greater than 10% for only 4 of 13 unique combinations of herbicide-site-year. Spray drift is probably the most common source of inadvertent application of herbicide to sensitive crops; generally, only a fraction of the herbicide dosage applied on an adjacent crop drifts off-target. The results of this study indicate that for any of the three herbicides investigated on canola and white bean, it is difficult to accurately predict eventual crop yield loss based on early season sublethal herbicide injury symptoms due to site-year variability and the potential for crop recovery and compensatory growth. This response was particularly true for white bean in this study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".