Weed Control, Environmental Impact and Profitability of Weed Management Strategies in
Bibliographic record
Abstract
Eleven field trials were conducted over a three-year period (2006-2008) at three locations in southwestern Ontario, Canada to evaluate the effect of various weed management strategies in glyphosate-resistant corn on weed control, crop injury, corn yield, environmental impact and profit margin. No visible injury resulted from the herbicide treatments evaluated. Overall, the effect of all factors assessed were location specific. By 56 days after treatment, depending on location, glyphosate applied at the 7-8 leaf stage (LPOST), preemergence (PRE) herbicides followed by (fb) glyphosate LPOST and sequential glyphosate applications (EPOST (3-4 leaf stage) followed by LPOST) provided more consistent control of annual broadleaf weeds and annual grasses compared to glyphosate applied alone EPOST. Weed control at 56 days after treatment was lower when glyphosate was applied alone LPOST compared to sequential applications of glyphosate or PRE herbicides fb glyphosate. There were no differences in corn yield among the sequential programs evaluated; however, a yield benefit was found when a sequential program was used compared to glyphosate applied alone LPOST. Among the sequential programs the lowest environmental impact was isoxaflutole/atrazine fb glyphosate. The lowest profit margins were associated with atrazine, S-metolachlor/atrazine/benoxacor, dicamba/atrazine and gly-phosate LPOST treatments compared to all other treatments. Overall, profit margins tended to be somewhat higher for treatments that included glyphosate applications. Based on these results, the most efficacious and profitable weed man-
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".