Optimization of a Mass Trapping Method against the Striped Cucumber Beetle Acalymma vittatum in Organic Cucurbit Fields
Bibliographic record
Abstract
(F.) (Coleptera: Chrysomelidae) is a prime problem in North American cucurbit crops. While certain chemical pesticides efficiently control SCB in conventional cucurbit fields, alternative solutions are required due to the ever-evolving regulations on pesticides. For organic producers, very few control methods exist. A novel mass trapping method demonstrates the potential of controlling SCBs using floral-based semiochemical baited traps in cucurbit crops. The goals of this study were to (1) determine whether baited traps capture more SCBs than unbaited ones, and (2) optimize the trapping method by comparing different trap types and different commercially available attractants to maximize SCB captures while minimizing non-target species captures. The results of a first experiment showed that baited traps captured significantly more SCBs than unbaited ones. Baited traps also captured significantly more bees and hoverflies than unbaited ones. In a second experiment these unwanted captures were drastically reduced by using traps with ten 4 mm in diameter holes per side. Finally, a third experiment demonstrated that the attractant 40CT313 was the most efficient at capturing SCB compared to other tested lures. Overall, the optimized mass trapping technique demonstrated a potential to effectively control SCB populations in organic cucurbit crops.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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) | 0.002 | 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 teacher head, 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".