Response of Processing Tomato to Simulated Bromoxynil Drift Followed by In-Crop Metribuzin Application
Bibliographic record
Abstract
Simulated drift rates of bromoxynil followed by an in-crop application of metribuzin were applied to processing tomato in eight field studies conducted from 2008 to 2010 in Ridgetown, Ontario, Canada, to determine if a synergistic interaction occurred due to the cumulative herbicide application. A transient synergistic response was observed 7 d after treatment (DAT) when bromoxynil drift rates of 8.5, 17, and 34 g ai ha −1 were followed 3 to 5 d later by metribuzin at 250 g ai ha −1 . By 28 DAT, visible injury ratings were additive for 8.5, 17, and 34 g ha −1 bromoxynil followed by metribuzin treatments. However, when bromoxynil at 68 g ha −1 (20% of field rate) was followed by metribuzin, a synergistic interaction was evident and remained through harvest. Based on Colby's equation there was greater visible injury than expected at 7, 14, and 28 DAT when bromoxynil at 68 g ha −1 was followed by metribuzin. A corresponding synergistic reduction of plant dry weight and marketable tomato yield, compared with the nontreated control, was identified. Marketable yields were expected to be 65% of the control according to Colby's equation, but observed yield reductions were 49% when bromoxynil at 68 g ha −1 was followed by metribuzin. In general, tomato plants sprayed with metribuzin after bromoxynil drift had greater injury than treatments sprayed with bromoxynil alone.
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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 | 0.002 |
| 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".