Effect of Halosulfuron Rate and Application Timing on Volunteer Azuki Bean Control in White Bean
Bibliographic record
Abstract
Three field experiments were carried out during 2017-2019 at the University of Guelph Huron Research Station near Exeter, Ontario, Canada to determine the effect of halosulfuron rate (25, 37.5 or 50 g∙ai∙ha-1) and application timing (POST 1, POST 2 and POST 3) on volunteer azuki bean control in white bean. At POST 1, halosulfuron at 25, 37.5 and 50 g∙ai∙ha-1 controlled volunteer azuki bean 46% - 50% at 1 week after application (WAA), controlled decreased to 16% - 25% at 8 WAA. At POST 2, volunteer azuki bean controlled decreased from 34% - 39% at 1 WAA to 17% - 27% at 8 WAA. A similar trend was observed at POST 3. Halosulfuron applied POST 1 at 25, 37.5 and 50 g∙ai∙ha-1 reduced biomass 49%, 64% and 69%, respectively. Halosulfuron applied POST 2 did not reduce volunteer azuki bean biomass at 25 g∙ai∙ha-1, but decreased biomass 51% at 37.5 g∙ai∙ha-1 and 49% at 50 g∙ai∙ha-1. Similarly, halosulfuron applied POST 3 did not reduce volunteer azuki bean biomass at 25 g∙ai∙ha-1, but decreased biomass 40% at 37.5 g∙ai∙ha-1 and 44% at 50 g∙ai∙ha-1. There was as much as 19%, 22% and 25% dockage with halosulfuron applied POST 1, POST 2 and POST 3, respectively. Volunteer azuki bean interference reduced white bean yield 40%. Reduced volunteer azuki bean interference with halosulfuron applied POST 1 at 37.5 or 50 g∙ai∙ha-1 resulted in an increase in white bean yield relative to the weedy control; however white yield was less than the weed-free control. This study concludes that halosulfuron at rates and application timings evaluated does not provide adequate control of volunteer azuki bean in 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".