Processing Tomato and Weed Response to Flufenacet plus Metribuzin
Bibliographic record
Abstract
Field experiments were conducted in 1998, 1999, and 2000 at two locations (Harrow and Ridgetown) in southwestern Ontario to determine the biologically effective rates (I 90 ) of a commercial formulation of flufenacet plus metribuzin for weed control and processing tomato tolerance. At the proposed label use rate, flufenacet plus metribuzin provided excellent (≥90%) early-season (22 to 29 d after planting) control of velvetleaf, good (80 to 89%) control of barnyardgrass and redroot pigweed, and fair (60 to 79%) control of common lambsquarters. Flufenacet plus metribuzin provided fair late-season (59 to 97 d after planting) control of redroot pigweed and common lambsquarters and poor (≤59%) control of barnyardgrass and velvetleaf. At Harrow and Ridgetown, I 90 values for early-season weed control ranged from 70 to 1,300 g ai/ha and 50 to 1,900 g ai/ha, respectively. Flufenacet plus metribuzin provided poor weed control at Ridgetown. This result was not attributable to higher weed density or particular weed species but may have been caused by lack of rainfall and too low application rates for the medium-textured soil type. It is estimated that flufenacet plus metribuzin at 1,400 g/ha can control green foxtail season-long, whereas barnyardgrass and common lambsquarters would require 1,900 g/ha. Season-long control of velvetleaf and redroot pigweed would require application rates of 3,200 and 7,100 g/ha, respectively. Only slight early-season crop injury was observed, which was not reflected in yields. Optimum yields of tomatoes were obtained at Harrow at rates lower or slightly higher than the registered rates for corn and soybean. Tomato yields were higher at Harrow than at Ridgetown, which may have been due to differences in soil texture. Tomatoes grown in a medium-textured (Ridgetown) soil appeared to be less competitive against weeds than those grown in a coarse-textured soil (Harrow).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".