Weed Management in Spring Planted Cereals with Mesotrione
Bibliographic record
Abstract
There is little information on the efficacy of mesotrione for the control of broadleaved weeds in spring planted cereals under Ontario environmental conditions. A total of eight studies were conducted in Ontario over a two-year period (2010 to 2011) to evaluate cereal tolerance and weed control efficacy of mesotrione applied preemergence (PRE) at 25, 50, 100, 140, and 280 g ai ha-1 in spring planted barley, durum wheat, oats, and wheat. Mesotrione, applied preemergence at the rates evaluated, caused no injury in either year in spring planted barley, durum wheat, oats, or wheat evaluated at 1, 2 and 4 week after emergence (WAE). The predicted mesotrione rate required to give adequate control of AMBEL, CHEAL, POLCO and SINAR was generally greater than 280 g ai ha-1. The average yield of the weedy check was 81% of the weed-free check. According to the exponential to maximum regression, the mesotrione rates required to give 90%, 95% and 98% of the weed-free check were 15, 30 and 45 g ai ha-1, respectively. To provide yield equivalent to the standard treatment of bromoxynil/MCPA, 36 g ai ha-1 of mesotrione was needed. Based on these results, mesotrione applied preemergence at 25, 50, 100, 140, and 280 g ai ha-1 can be safely used in spring planted barley, durum wheat, oats, and wheat. However, greater than 280 g ai ha-1 of mesotrione was needed to adequately control AMBEL, CHEAL, POLCO and SINAR.
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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".