Weed Control in Processing Tomato (Lycopersicon esculentum) with Rimsulfuron and Thifensulfuron Applied Alone or with Chlorothalonil or Copper Pesticides
Bibliographic record
Abstract
Combining herbicides and fungicides can improve production efficiency; however, there is little information on the effect of these mixtures on weed control and processing tomato crop response. Six field trials were conducted from 2002 to 2004 in Ontario to study the effect of rimsulfuron and thifensulfuron applied alone or in combination with metribuzin and with or without chlorothalonil or copper fungicides on processing tomato. There was no visual injury or reduction in marketable yield of processing tomato with rimsulfuron or thifensulfuron alone or when tank-mixed with chlorothalonil or copper hydroxide. Rimsulfuron, thifensulfuron, rimsulfuron plus metribuzin, and thifensulfuron plus metribuzin could be tank-mixed with chlorothalonil without a reduction in weed control. However, efficacy of rimsulfuron and thifensulfuron were reduced when tank-mixed with copper hydroxide. The reduction in weed control incited by adding copper hydroxide was overcome with a low rate (150 g·ha –1 a.i.) of metribuzin for thifensulfuron but not rimsulfuron. Application of rimsulfuron and thifensulfuron alone or with low rates of metribuzin and chlorothalonil could provide tomato growers with a single-pass treatment for the control of troublesome weeds and diseases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".