Tolerance of black, cranberry, kidney, and white bean to cloransulam‐methyl
Bibliographic record
Abstract
The level of tolerance of various market classes of dry bean to cloransulam‐methyl is not known. Three field studies were conducted in Ontario, Canada during 2007 and 2008 to determine the level of tolerance of black, cranberry, kidney, and white bean to the pre‐emergence (PRE) and postemergence (POST) application of cloransulam‐methyl at 17.5, 35, and 70 g ai ha−1. Cloransulam‐methyl applied at 17.5, 35, and 70 g ha−1 caused between 13 and 23% injury in black, cranberry, kidney, and white bean, respectively. Cloransulam‐methyl applied at 17.5, 35, and 70 g ha−1 reduced the shoot dry weight by between 16 and 28% compared to the untreated control. Cloransulam‐methyl applied PRE reduced the height of black bean by 27% and the height of cranberry bean by 25% at 70 g ha−1 and reduced the height of white bean by 19% at 35 g ha−1 and by 37% at 70 g ha−1. Cloransulam‐methyl applied PRE reduced the yield of black bean by 29% at 35 g ha−1 and by 43% at 70 g ha−1, reduced the yield of cranberry bean by 43% at 70 g ha−1, and reduced the yield of white bean by 36% at 35 g ha−1 and by 54% at 70 g ha−1. Based on these results, there is not an adequate margin of crop safety for the PRE and POST application of cloransulam‐methyl in black, cranberry, kidney, and white bean at the rates evaluated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".