Bright ideas: comparison of LED and black-light fluorescent light performance on the capture of macromoth assemblages in western Newfoundland’s boreal forest, Canada
Bibliographic record
Abstract
Abstract Moths are a hyperdiverse taxon and contribute to important ecosystem services, including herbivory, pollination, and as food for other animals. Artificial light is an effective means by which to attract nocturnal moths for ecological study, but many traditional light-trapping approaches require the use of heavy, lead acid batteries, whereas novel light-emitting diodes (LEDs) use much lighter and energy-efficient lithium-ion batteries. Employing replicated forest stands being used for a longer-term study on the effects of Bacillus thuringiensis subsp. kurstaki (Btk) application, we assessed how traps fitted with either black-light fluorescent (BLF) or LED lights differed in the moth assemblages they attracted. The macromoth assemblages captured by the two light sources differed significantly in their composition, with some species almost exclusively collected by a particular light type. We collected significantly more moths in the BLF traps overall. However, we found a higher diversity of species using the LED light traps but only in the Btk–treated sites. We show that, although these lights appear to attract significantly different species assemblages, LEDs represent an effective, efficient, and environmentally safer approach for attracting macromoths. More empirical studies will help elucidate which species are most attracted to various light sources and if broader phylogenetic patterns exist.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".