OlfactionROOM: An optimised, low‐cost olfactometer and easy‐to‐apply setup to mitigate the escape behaviour of insects
Bibliographic record
Abstract
Abstract Olfactometers are a widely used tool to investigate the effects of volatile organic compounds (VOCs) on insect behaviour. However, conventional olfactometers are of limited use for highly mobile, walking insects that exhibit a strong escape behaviour, resulting in biased measurements due to insects' agitation at the beginning of the bioassay. To minimise agitation of highly mobile walking insects, we designed the OlfactionROOM‐olfactometer, an improved four‐arm olfactometer, featuring a central chamber where insects are exposed to all test odours before the start of the bioassay. Additionally, a simple and remote deactivation of the chamber reduces the operator's influence on insect behaviour at the onset of the bioassay and allows them to adapt to the experimental environment. To evaluate the OlfactionROOM's effectiveness, we conducted trials using plant‐derived VOCs as lures, and Agriotes sputator click beetles (Coleoptera: Elateridae) as a model species for highly mobile, walking insects. We compared the beetles' mobility rates and average moving speed between the OlfactionROOM and two conventional olfactometers as indicators of agitation. Additionally, we tested the performance of the optimised olfactometer and our easy‐to‐apply setup on the first choice of A. sputator beetles when exposed to its sex pheromone as an attractant and basil oil as a repellent. Beetle average moving speed and mobility rate were significantly reduced by 37.3% and 35.2% respectively in the OlfactionROOM compared to standard olfactometers, indicating reduced levels of insect agitation during data acquisition. The OlfactionROOM‐olfactometer combined with a simple mechanism for remote deactivation of the insect acclimation chamber enabled a distinct measurement of the beetles' first‐choice responses to basil oil and sex pheromone, showing significant differences between the two ( p = 0.35). Furthermore, the improved olfactometer setup enhanced the quality of video recordings due to the remote deactivation of the acclimation chamber, allowing uninterrupted observation of insect behaviour. Our findings demonstrate that the OlfactionROOM and the easy‐to‐apply setup offer a low‐cost tool for improving the characterisation of the ecological role of VOCs in highly mobile insects by mitigating test insects' escape behaviour. Both the blueprints for manufacturing the OlfactionROOM‐olfactometer and the accompanying software are freely available, facilitating easy access and rapid implementation of this novel design.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".