Effect of Low Rate of Dicamba on Tomato (Solanum lycopersicum) at Different Growth Stages
Bibliographic record
Abstract
Tomatoes are highly sensitive to herbicides, and concerns have been raised regarding off-target movement of dicamba and 2,4-D with the advent of new technologies in crops like soybean and cotton. Greenhouse studies were conducted over two years to assess the effect of low rates of dicamba on tomatoes at different growth stages and investigate fruit contamination. Treatments included untreated controls and dicamba applied at 1/16X, 1/32X, 1/64X, and 1/128X rates with non-ionic surfactant (NIS). Tomato plants at three growth stages (vegetative, flowering, and fruiting) were evaluated for dicamba sensitivity. Vegetative stage plants showed the highest sensitivity, while no significant differences in injury were observed between flowering and fruiting stages. Only the untreated controls produced fruit at the vegetative stage. Plants at flowering and fruiting stages successfully produced fruits. Harvested tomato fruits from each dicamba rate and the untreated control were planted, and progeny (F1) seedlings were evaluated for dicamba symptomology. No visual dicamba symptoms were observed in the tomato progeny, indicating the absence of dicamba contamination. High-performance liquid chromatography analysis confirmed no detectable levels of dicamba in the fruit samples. These findings indicate that low rates of dicamba, even at simulated drift levels, do not significantly affect tomatoes or result in fruit contamination. The results contribute to understanding the risks associated with herbicide drift and its impact on sensitive crops like tomatoes.
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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".