Chemical light sticks as bait to trap predaceous aquatic insects: effect of light colour
Bibliographic record
Abstract
We measured the efficiency of floating minnow-traps (Gee traps), unbaited or baited with red, green, blue, or white chemical light sticks (glowsticks) to trap large, predaceous, aquatic beetles (Coleoptera) and bugs (Hemiptera) in 50 fresh water, lentic systems in northern Nova Scotia, Canada. Standard minnow-traps buoyed with Styrofoam® floats were set overnight in a variety of freshwater habitats, including ponds, marshes, bog pools and vegetated lake margins throughout the ice-free seasons over three years, for a total of 695 trap-nights. Giant Water Bugs (Lethocerus americanus) were captured with equal frequency in traps baited with any colour glowstick and in unbaited controls. Brown Waterscorpions (Ranatra fusca) were significantly more abundant in light-baited traps than in dark controls, and showed a strong preference for green lures over other colours. The large, Vertical Diving Beetle Dytiscus verticalis was caught significantly more than expected with white or red lures and significantly less with green or blue lures; males were caught even less often with green or blue lures than in unbaited controls. Our results reveal a heretofore unknown component of the biology of these insects, and suggest a novel method for simple and effective sampling of aquatic insects in still waters.Keywords: light; trapping; phototaxis; aquatic insects; colour
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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 | 0.000 |
| 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".