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−1were 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−1bromoxynil 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−1was 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−1was 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 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".