Effect of foliar applications of neem oil and fish emulsion on bacterial spot and yield of tomatoes and peppers
Bibliographic record
Abstract
Foliar applications of neem oil and fish emulsion, derived from neem seed and menhaden fish, respectively, were tested for their ability to reduce bacterial spot of tomato and bell pepper under both greenhouse and field conditions. Greenhouse-grown tomato and pepper plants sprayed with aqueous suspensions (0.5%, v/v) of neem oil or fish emulsion and then inoculated with Xanthomonas campestris pv. vesicatoria showed less disease symptoms than the water-treated controls. Weekly foliar sprays of neem oil and fish emulsion reduced disease severity on the foliage of inoculated field-grown tomato and pepper plants in both years of a two-year study. The disease incidence on the fruit of these plants was reduced but the effect was not always statistically significant. However, the number of lesions per pepper fruit were consistently lower with these treatments. Fish emulsion increased healthy and total fruit yield of tomatoes in 2000 and healthy fruit yield of peppers in 2001, whereas neem oil increased the yield of disease-free peppers in both years. Neem oil and fish emulsion had no observable phytotoxic effect on tomato or pepper foliage in the field. These results suggest that disease-management programs for bacterial spot may be enhanced by including foliar sprays of these products.
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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) | 0.000 | 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".