The First Standardised Sampling Plan Designed to Scout <i>Dalbulus maidis</i> (Hemiptera: Cicadellidae) Adults in Corn Crops Using Yellow Sticky Traps
Bibliographic record
Abstract
ABSTRACT The corn leafhopper Dalbulus maidis (Hemiptera: Cicadellidae) is one of the main pests in corn crops causing yield reductions of up to 70%. Dalbulus maidis primarily damages corn by transmitting pathogens. Direct plant damage is caused by sap suction and toxin injection. Sampling corn fields to determine the D. maidis density for decision‐making systems can be challenging due to field size, time and cost. Yellow sticky traps (YST) are commonly employed to determine the presence and density of D. maidis because they are easy to use and allow quick and low‐cost assessment. This study aimed to propose and evaluate a new sampling plan for D. maidis in corn crops using YST. This research was carried out in commercial corn crops in the Brazilian Atlantic Forest and Cerrado biomes for 3 years. The pest density in traps and on plants were moderately (r = 0.54–0.66) and significantly (p < 0.0001) correlated. The density evaluated in the traps showed relative variance of less than 25% and sampling time of up to 2 min per sample unit. The negative binomial distribution was adequate to represent the D. maidis density probability distribution for the YST sampling method with K common (2.8979) among all the fields evaluated. The sampling plan consisted of installing one trap every two hectares. The plan's total cost ranged from US$/ha 0.41 to 0.50, with a total sampling time of up to 2.5 min/ha. We propose this new sampling plan for D. maidis, which is suitable for incorporation into management programmes in corn crops, as it is representative, accurate, fast and low cost.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".