Snap Bean Tolerance to Herbicides in Ontario
Bibliographic record
Abstract
Snap bean was evaluated for sensitivity to a number of herbicides in field studies conducted during a 2-yr period in Exeter, ON. Preemergence (PRE) applications of metolachlor (1,600 and 3,200 g ai/ha), imazethapyr (75 and 150 g ai/ha), and clomazone plus metobromuron (840 + 1,000 g ai/ha and 1,680 + 2,000 g/ha) were evaluated for visual injury at 7, 14, and 28 d after emergence. Postemergence (POST) applications of imazamox plus fomesafen (25 + 200 g ai/ha and 50 + 400 g/ha), quizalofop-P (72 and 144 g ai/ha), and clethodim (90 and 180 g ai/ha) also were evaluated for visual injury 7, 14, and 28 d after treatment. Plant height and crop yield were assessed for all treatments. Visual injury, stunting, and yield loss were not observed in the metolachlor treatments. Imazethapyr (150 g/ha) caused stunting and reduced snap bean yield in both study years. Clomazone plus metobromuron (1,680 + 2,000 g/ha) injured and stunted snap bean in both years of the study and reduced yield in 2000. Imazamox plus fomesafen (50 + 400 g/ha) injured snap bean in both years but only reduced yield in 2000. Quizalofop-P injured snap bean but did not reduce plant height or yield. Clethodim did not injure, stunt, or reduce yield of snap bean. Metolachlor (PRE), imazamox plus fomesafen (POST), quizalofop-P (POST), and clethodim (POST) have excellent potential as weed management tools in snap bean in Ontario.
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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 | 0.001 | 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".