Time of Day Impacts Postemergence Weed Control in Corn
Bibliographic record
Abstract
Field trials were conducted from 2005 to 2007 at two locations in southwestern Ontario to investigate how weed control in corn was affected by the time of day that herbicides were applied. Weed control following the application of six POST herbicides (atrazine, bromoxynil, dicamba/diflufenzopyr, glyphosate, glufosinate, and nicosulfuron) at 06:00, 09:00, 12:00, 15:00, 18:00, 21:00, and 24:00 h was assessed. For many weed species herbicide efficacy was reduced when applications were made at 06:00, 21:00, and 24:00 h. Velvetleaf was the most sensitive to the time of day effect, followed by common ragweed, common lambsquarters, and redroot pigweed. Annual grasses were not as sensitive to application timing; however, control of barnyardgrass and green foxtail was reduced in some environments at 06:00 h and after 21:00 h. Only in the most severe cases was the grain yield of corn reduced due to decreased weed control. Daily changes in air temperature, relative humidity, and light intensity that cause species-specific physiological changes may account for the variation in weed control throughout the day. The results of this research suggest that there is a strong species-specific influence of ambient air temperature, light intensity, and leaf orientation on the efficacy of POST herbicides. These results should aid growers in applying herbicides when they are most efficacious, thus reducing costs associated with reduced efficacy.
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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 | 0.001 |
| 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 | 0.000 | 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".