Response of dry beans to halosulfuron applied postemergence
Bibliographic record
Abstract
Soltani, N., Shropshire, C. and Sikkema, P. H. 2012. Response of dry beans to halosulfuron applied postemergence. Can. J. Plant Sci. 92: 723-728. Four field trials were conducted over a 2-yr period (2009 and 2010) at Exeter and Ridgetown, Ontario, to evaluate the tolerance of adzuki, black, cranberry, kidney, otebo, pinto, Small Red Mexican and white beans to halosulfuron applied postemergence (POST) at 35 and 70 g a.i. ha-1. All treatments including the non-treated control were maintained weed free during the growing season. Halosulfuron applied POST caused as much as 73, 7, 13, 12, 12, 11, 11 and 9% injury in adzuki, black, cranberry, kidney, otebo, pinto, Small Red Mexican (SRM) and white beans, respectively. Halosulfuron applied POST reduced adzuki bean height as much as 52 and 70% at Exeter and Ridgetown, respectively. Plant height was not affected in the other market classes of dry bean evaluated. Halosulfuron POST reduced shoot dry weight of adzuki bean 68% at both rates evaluated. Otebo and SRM bean shoot dry weight were not affected when halosulfuron was applied POST at 35 g a.i. ha-1 but otebo bean shoot dry weight was reduced 12% and SRM bean shoot dry weight was reduced 14% at 70 g a.i. ha-1. Shoot dry weight of black, cranberry, kidney, pinto and white bean was not affected with either rate of halosulfuron. Seed yield of adzuki bean was decreased 58% at 35 g a.i. ha-1 and 68% at 70 g a.i. ha-1 with halosulfuron. White bean yield was not affected with halosulfuron applied POST at 35 g a.i. ha-1 but was reduced 9% at 70 g a.i. ha-1. Seed yield of black, cranberry, kidney, otebo, pinto and SRM bean was not reduced with either rate of halosulfuron. Based on these results, there is not an adequate margin of crop safety for halosulfuron POST in adzuki bean. However, there is potential for POST application of halosulfuron in black, cranberry, kidney, otebo, pinto, SRM and white beans.
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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 | 0.001 |
| 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".