Co-application of herbicides and insecticides in dry bean
Bibliographic record
Abstract
Eight field trials were conducted from 2006 to 2008 at various locations in Ontario to evaluate the co-application of postemergence herbicides with cyhalothrin-lambda or dimethoate insecticides in cranberry and white bean. At 2 weeks after treatment, the addition of cyhalothrin-lambda or dimethoate insecticides to sethoxydim, quizalofop-p-ethyl, bentazon, fomesafen and bentazon plus fomesafen did not increase injury at the Exeter and Ridgetown locations except for bentazon plus dimethoate which caused greater injury than bentazon alone (2.9% vs 0.2%) in 2006. However at Harrow, the addition of dimethoate to quizalofop-p-ethyl increased injury (0% vs 4.9%) in 2007 and the addition cyhalothrin-lambda or dimethoate to sethoxydim increased injury in 2008 in dry bean. The addition of cyhalothrin-lambda to quizalofop-p-ethyl also increased injury (0% vs 4.5%) in 2008. There was no adverse effect on dry bean injury with other treatments at Harrow in 2007 or 2008. The addition of cyhalothrin-lambda or dimethoate to the herbicides evaluated did not have any adverse effect on plant height, shoot dry weight or yield of dry bean except for bentazon plus dimethoate which decreased shoot dry weight 20% compared to bentazon alone at Harrow in 2008. Based on these results, cyhalothrin-lambda or dimethoate can be tank-mixed with sethoxydim, quizalofop-p-ethyl, bentazon, fomesafen and bentazon plus fomesafen when the optimum application timing of these herbicides and insecticides coincide.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".