Weed Control with Halosulfuron Applied Preplant Incorporated, Preemergence or Postemergence in White Bean
Bibliographic record
Abstract
Four field trials were conducted over a three-year period (2011-2013) at various locations in Ontario to evaluate the level of weed control provided by halosulfuron applied PPI,PREor POST at 17.5, 35 and 70 g·ai·ha-1 in white bean. Halosulfuron applied PPI or PRE at 17.5, 35 and 35 g·ai·ha-1 caused 2% or less visible injury 1 and 4 WAA in white bean. However, halosulfuron applied POST at 17.5, 35 and 70 g·ai·ha-1 caused 2% - 8% and 1% - 3% white bean injury at 1 and 4 WAA, respectively. There was no decrease in white bean seed yield relative to the weed free check due to weed interference with halosulfuron applied PPI or PRE at doses evaluated, except when applied PRE at 17.5 g·ai·ha-1 which resulted in a decrease in seed yield of 25%. Weed interference caused a decrease in white bean yield of 47%, 42% and 44%, when halosulfuron was applied POST at 17.5, 35 and 70 g·ai·ha-1, respectively. Halosulfuron applied PPI, PRE and POST controlled AMARE 92% - 100%, 85% - 99% and 47% - 75%; CHEAL 95% - 100%, 83% - 99% and 36% - 51%; and SINAR 97% - 100%, 99% - 100% and 100%, respectively. Halosulfuron applied PPI and PRE reduced AMARE density 93% - 97% and 75% - 95%; CHEAL density 89% - 98% and 81% - 93%; and SINAR density 99% - 100% and 99% - 100%, respectively. Halosulfuron applied PPI and PRE reduced dry weight of AMARE 96% - 98% and 86% - 96%; CHEAL 96% - 98% and 87% - 93%; and SINAR 100% and 100%, respectively. Halosulfuron applied POST at rates evaluated reduced SINAR density and dry weight 100% but caused no significant reduction in AMARE and CHEAL density or dry weight compared to the weedy check. Based on these results, halosulfuron applied PPI orPREat 35 g·ai·ha-1 can be used safely for the control of selected broadleaf weeds in white bean production.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".