Chemical and mechanical weed management strategies for grain pearl millet and forage pearl millet
Bibliographic record
Abstract
There are limited options for controlling weeds in grain and forage pearl millet production in eastern Canada. Field studies were conducted near Quebec City in 2005 and 2006 to evaluate the effectiveness of herbicidal treatments (s-metolachlor/benoxacor and pendimethalin) and harrowing to control weeds and maximize yield in grain and forage pearl millet cultivation. s-Metolachlor/benoxacor and pendimethalin were applied either preemergence or early postemergence at the full dose recommended for corn and at half of that dose. Harrowing was evaluated at the three- and the five-leaf stage of pearl millet. In both types of pearl millet, s-metolachlor/benoxacor applied preemergence reduced plant density and yield. All other herbicidal treatments caused no visual injury to pearl millet and adequately controlled annual weeds, leading to grain and forage yields similar or slightly lower than those of the hand-weeded control. Better barnyard grass control resulted from early postemergence application of s-metolachlor/benoxacor compared with that of pendimethalin. Preemergence application of pendimethalin was more effective at controlling barnyard grass than early postemergence application. Harrowing was not effective at controlling annual weeds, resulting in important millet yield losses.Key words: Pearl millet, Pennisetum glaucum, s-metolachlor/benoxacor, pendimethalin, mechanical weed control, weed harrowing
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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".