Significance of Atrazine as a Tank-Mix Partner with Tembotrione
Bibliographic record
Abstract
Manufacturers of several POST corn herbicides recommend tank-mixing their herbicides with atrazine to improve performance; however, future regulatory changes may place greater restrictions on atrazine use and limit its availability to growers. Our research objectives were to quantify the effects of tank-mixing atrazine with tembotrione compared to tembotrione alone on (1) weed control, (2) variability in weed control, and (3) sweet corn yield components and yield variability. Field studies were conducted for 2 yr each in Illinois, Oregon, Washington, and Ontario, Canada. Tembotrione at 31 g ha−1 was applied alone and with atrazine at 370 g ha−1 POST at the four- to five-collar stage of corn. The predominant weed species observed in the experiment were common to corn production, including large crabgrass, wild-proso millet, common lambsquarters, and velvetleaf. For nearly every weed species and species group, the addition of atrazine improved tembotrione performance by increasing mean levels of weed control 3 to 45% at 2 wk after treatment. Adding atrazine reduced variation (i.e., standard deviation) in control of the weed community by 45%. Sweet corn ear number and ear mass were 9 and 13% higher, respectively, and less variable when atrazine was applied with tembotrione, compared to tembotrione alone. Additional restrictions or the complete loss of atrazine for use in corn will necessitate major changes in sweet corn weed management systems.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".