An Integrated Weed Management Strategy for Glufosinate-Resistant Corn (Zea mays)<sup>1</sup>
Bibliographic record
Abstract
Early canopy closure and manipulation of crop row spacing or density can reduce the amount and frequency of herbicide use in corn. Field studies were conducted at Woodstock, ON from 1996 to 1999 to evaluate the effect of corn row spacing, plant density, and frequency of glufosinate application on weed biomass and corn yield in glufosinate-resistant corn. Treatments included row width, corn density, and herbicide. The effect of row width and corn density on weed biomass was variable among years. In a wet year (1996), narrow (38 cm) rows provided greater weed suppression than wide (76 cm) rows regardless of crop density. In a dry year (1998), narrow-row high-density (100,000 plants/ha) corn had the lowest weed biomass. In other years, either narrow row or high density was equally successful in suppressing weeds. Effectiveness of herbicides in reducing weed biomass was not influenced by row width or corn density. Corn yield was influenced by row width or corn density. Although weed biomass was lowered by two applications of glufosinate in comparison with a single application, corn grain yields were similar between the two treatments. Planting corn at higher densities may help in reducing early-season weed competition, whereas narrow rows may help in controlling later-emerging species.Nomenclature: Glufosinate; glyphosate; metolachlor; dicamba; corn, Zea mays L. Pride X2650LL.Additional index words: Corn row spacing, corn density.Abbreviations: EPOST, early postemergence; IWM, integrated weed management; LPOST, late postemergence; POST, postemergence.
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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".