Factors influencing the capture of Japanese beetles: wind speed and direction, trap deployment protocol, lure type, and trap efficiency
Bibliographic record
Abstract
Abstract In this study, factors affecting Japanese beetle (JB) trap captures (eg wind direction and speed, lure type, trap deployment and efficiency) were investigated in the field using semiochemical baited traps. Fifty-two percent of 667 JB observed flew directly upwind toward a trap with a dual lure (sex pheromone + floral kairomone), especially when wind speed exceeded 1.5 m/s. Within thirty seconds after approaching 2 m of a trap baited with a dual lure, 43%, 35%, and 22% of 138 observed beetles were captured, flew away, or landed on the trap, respectively. Of 265,884 beetles captured, 48% were captured by a cluster of 3 traps each with a single dual lure, 30% by a single trap baited with 3 dual lures, and 22% by a single trap baited with one dual lure. Traps baited with the floral lure (ie kairomone) and the floral lure and the sex pheromone (ie dual lure) captured similar numbers (39% and 41%, respectively) of JB and both captured more beetles than traps baited with the pheromone alone (12% and 8% for the sex-pheromone from the dual lure and sex-pheromone standard, respectively). Traps baited with the floral kairomone alone or with sex pheromone captured a 1:1 sex ratio (M:F) while pheromone-only traps captured a 3:1 (M:F) ratio of JB. No additive or synergistic effects of the pheromone and floral kairomone were observed. To our knowledge, this study is the first to quantify in real time the flight behavior of a beetle toward traps in the field.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.001 |
| 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".