Identification of Empoasca onukii (Hemiptera: Cicadellidae) and Monitoring of its Populations in the Tea Plantations of South China
Bibliographic record
Abstract
Tea green leafhoppers (Empoasca spp.) are considered one of the major pests in tea plantations in Asia. They are, however, difficult to monitor due to their size and flying and jumping abilities. In this study, we clarified the identification of the leafhopper species encountered in our study plantations and examined the impacts of sampling methods in estimating population abundance and sex ratio. The natural sex ratio of eggs, nymphs, and adults of tea green leafhopper and the differences between male and female were tested. Despite previous reports that Empoasca vitis (Goethe) was the major leafhopper present in our study area, our results showed that only Empoasca onukii Matsuda was found. Variation in population size over time and bias in sex ratio depending on the sampling methods were found in our monitoring experiments. In general, adult males were more attracted to yellow sticky cards than females. We believe that because female leafhoppers should be the target in pest control, yellow sticky cards may not be the most suitable monitoring or effective control of tea green leafhopper. We demonstrate the importance of understanding the implications of sampling techniques for population estimation and sex ratio bias as well as how temporal variation may affect monitoring results. Precise monitoring should take into consideration the different life histories of male and female.
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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 | 0.001 | 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) | 0.000 | 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".