Are Yellow Sticky Cards and Light Traps Effective on Tea Green Leafhoppers and Their Predators in Chinese Tea Plantations?
Bibliographic record
Abstract
In Chinese tea plantations, yellow sticky cards and light traps are increasingly used to control insect pests, especially the tea green leafhopper Empoasca onukii. In this study, a 16-week open-field experiment with daily weather monitoring was designed to test the responses of tea green leafhopper, parasitoids and spiders to yellow sticky cards and three light traps with different wavelengths (covered with sticky cards). An exclosure experiment was also designed to further test the influence of the three light systems (without sticky card) on the same species. The results showed that all three light emitting diode (LED) light traps (white, green and yellow) and yellow sticky cards attracted many more E. onukii male adults than females during the course of the open field experiment, with less than 25% of trapped adults being females. Parasitoids and spiders were also attracted by these systems. Weather variables, especially rainfall, influenced the trapping efficiency. In the exclosure experiment, the population of leafhoppers in the yellow sticky card treatment did not decline significantly, but the number of spiders significantly decreased. The green and white light treatments without sticky cards showed a significant control of E. onukii and no obvious harm to spiders. These results suggest that yellow sticky cards and light traps have limited capacity to control tea green leafhoppers. However, light, especially green light, may be a promising population control measure for tea green leafhoppers, not as killing agents in the traps, but rather as a behavioral control system.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".