Weed Control in White Bean with Pendimethalin Applied Preplant Followed by Postemergence Broadleaved Herbicides
Bibliographic record
Abstract
Field trials were conducted over a three-year period (2009 to 2011) to evaluate the efficacy of pendimethalin preplant-incorporated (PPI), bentazon, fomesafen, bentazon plus fomesafen, or halosulfuron applied postemergence (POST) and the sequential application of pendimethalin applied PPI followed by bentazon, fomesafen, bentazon plus fomesafen or halosulfuron applied POST in white bean in Ontario. There was minimal effect on seed moisture content of white bean with the herbicides evaluated. Pendimethalin provided 97% control of A. retroflexus, 9% of A. artemisiifolia, 90% of C. album, 12% of S. arvensis, and 96% of S.viridis. Bentazon, fomesafen, bentazon plus fomesafen, and halosulfuron applied POST provided as much as 93% control of A. retroflexus, 86% control of A. artemisiifolia, 72% control of C. album, 99% control of S. arvensis, and 29% control of S. viridis. The sequential application of pendimethalin applied PPI followed by bentazon, fomesafen, bentazon plus fomesafen, and halosulfuron applied POST provided 100% control of A. retroflexus, 87% control of A. artemisiifolia, 90% control of C. album, 100% control of S. arvensis, and 95% control of S .viridis, respectively. White bean yield generally reflected the level of weed control.
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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 | 0.000 | 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) | 0.001 | 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".