Sensitivity of Leguminous Crops to Saflufenacil
Bibliographic record
Abstract
There is little information on the tolerance of leguminous crops to saflufenacil. A field study was conducted three times over a 2-yr period (2006, 2007) in Ontario, Canada, to determine the tolerance of adzuki bean, cranberry bean, lima bean, processing pea, snap bean, soybean, and white (navy) bean to saflufenacil applied PRE at 100 and 200 g ai/ha. Saflufenacil caused 51 to 99% injury, reduced height 25 to 93%, reduced shoot dry weight 92 to 99%, and reduced seed yield 56 to 99% in adzuki bean, cranberry bean, lima bean, snap bean, and white bean. Injury was lower in soybean and processing pea. Saflufenacil caused 1 to 25% injury, reduced height 3 to 13%, reduced shoot dry weight 5 to 30%, and reduced seed yield 0 to 4% in soybean and processing pea. Cranberry bean, snap bean, white bean, and lima bean were the most sensitive crops to saflufenacil followed by adzuki bean. Soybean and processing pea were the most tolerant to saflufenacil. Based on these results, saflufenacil applied PRE can be safely used in specific cultivars of pea and soybean at the proposed rate of 100 g/ha. However, there is not an acceptable margin of crop safety for saflufenacil PRE at 100 or 200 g/ha in adzuki, cranberry, lima, snap, and white bean.
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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".