Feasible sampling plan for the whitefly <i>Bemisia tabaci</i> in bell pepper crops
Bibliographic record
Abstract
Abstract Bell peppers ( Capsicum annuum ) play a key role in food production, commerce, and society, with smallholder farmers being the 36 primary cultivators. However, the whitefly Bemisia tabaci (Hemiptera: Aleyrodidae) poses a significant threat to bell pepper crops. Traditional control methods rely heavily on the application of insecticides, resulting in increased production costs and ecological concerns. To address this issue, the establishment of decision‐making systems, starting with effective sampling plans, is crucial. This study aimed to develop a practical sampling strategy for assessing B. tabaci populations at different growth stages of bell pepper crops, including vegetative, flowering, and fruiting stages. Over a 4‐year period, commercial bell pepper fields were monitored to determine the optimal sampling technique and sample size. Results indicated that sampling the apical third of the plant's leaves and shaking the plants onto a white plastic tray yielded the most accurate samples. Pest densities followed a negative binomial distribution pattern, with a consistent aggregation parameter (Kc = 0.3339) across all fields. Therefore, assessing 78 plants per field was deemed necessary. The sampling procedure incurred a cost of up to $1.12 per hectare and required approximately 24 min. The simplicity, ease of execution, and low cost of the developed sampling strategy make it suitable for integration into comprehensive pest management programs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".