A Guide for Using Flight Simulators to Study the Sensory Basis of Long-Distance Migration in Insects
Bibliographic record
Abstract
Studying the routes flown by long-distance migratory insects comes with the obvious challenge that the animal's body size and weight is comparably low. This makes it difficult to attach relatively heavy transmitters to these insects in order to monitor their migratory routes (as has been done for instance in several species of migratory birds. However, the rather delicate anatomy of insects can be advantageous for testing their capacity to orient with respect to putative compass cues during indoor experiments under controlled conditions. Almost 20 years ago, Barrie Frost and Henrik Mouritsen developed a flight simulator which enabled them to monitor the heading directions of tethered migratory Monarch butterflies, both indoors and outdoors. The design described in the original paper has been used in many follow-up studies to describe the orientation capacities of mainly diurnal lepidopteran species. Here we present a modification of this flight simulator design that enables studies of nocturnal long-distance migration in moths while allowing controlled magnetic, visual and mechanosensory stimulation. This modified flight simulator has so far been successfully used to study the sensory basis of migration in two European and one Australian migratory noctuid species.
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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) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.062 | 0.050 |
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".