Integrated weed management in white bean production
Bibliographic record
Abstract
A study was conducted over a 3-yr period (2004–2006) in Ontario to evaluate various weed management programs in white bean (Phaseolus vulgaris L.). Herbicide treatments evaluated caused no visible injury in white bean. Trifluralin provided 12% (percentage points) greater control of common lambsquarters (Chenopodium album L.) than s-metolachlor. There was no benefit of tank-mixing s-metolachlor and trifluralin for yield and profitability compared with either trifluralin or s-metolachlor alone. The postemergence (POST ) application of bentazon plus fomesafen following a soil-applied herbicide resulted in improved control of common lambsquarters by 15%. Two inter-row cultivations following a soil-applied herbicide resulted in improved control of redroot pigweed (Amaranthus retroflexus L.), common lambsquarters, and green foxtail [Setaria viridis (L.) Beauv.]. The addition of imazethapyr (60% of label dose; 45 g a.i. ha-1) to the soil-applied herbicide resulted in improved control of redroot pigweed (+6%), common lambsquarters (+16%), and green foxtail (+6%). The profit margin tended to increase if more than just a grass preplant-incorporated (PPI) herbicide was used. The best profit margin was with a grass PPI herbicide alone plus cultivation. The profit margin also tended to increase with the use of cultivation rather than a broadleaf POST herbicide. Key words: Bentazon, cultivation, fomesafen, imazethapyr, navy bean, s-metolachlor, trifluralin, Phaseolus vulgaris L.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".