Weed Management in White Bean with Variable Doses of S-Metolachlor and Halosulfuron Applied Preemergence
Bibliographic record
Abstract
Five experiments were conducted in Ontario, Canada from 2016 to 2018 to determine how doses of S-metolachlor and halosulfuron applied preemergence (PRE) should be adjusted to control specific weed species in white bean. S-metolachlor, halosulfuron, and S-metolachlor + halosulfuron caused minimal (1% to 4%) injury in white bean. Weed interference reduced white bean yield 54%. On average, weed interference with S-metolachlor and halosulfuron decreased yield 34% and 29%, respectively. In contrast, white bean seed yield was similar to the weed-free control with the S-metolachlor + halosulfuron tankmixes. S-metolachlor applied alone controlled A. theophrasti, A. retroflexus, A. artemisiifolia, C. album, E. crus-galli and S. viridis 0% to 3%, 78% to 93%, 0% to 9%, 5% to 15%, 97% to 99% and 96% to 98%, respectively. Halosulfuron applied alone controlled A. theophrasti, A. retroflexus, A. artemisiifolia, C. album, E. crus-galli and S. viridis 39% to 87%, 93% to 99%, 64% to 88%, 34% to 59%, 10% to 30% and 13% to 35%, respectively. S-metolachlor + halosulfuron tankmixes controlled A. theophrasti, A. retroflexus, A. artemisiifolia, C. album, E. crus-galli and S. viridis 47% to 94%, 98% to 100%, 78% to 94%, 37% to 78%, 94% to 98% and 91% to 96%, respectively. Weed density and biomass reductions with the herbicides evaluated followed the same pattern as visible weed control assessments. Results from this study indicate that doses of S-metolachlor and halosulfuron, when applied as a tankmix, should be adjusted based on a weed species composition in each individual white bean field.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".