Bibliographic record
Abstract
The behaviours and cognitive mechanisms animals use to orient, navigate, and remember spatial locations exemplify how cognitive abilities have evolved to suit a number of different mobile lifestyles and habitats. While spatial cognition observed in vertebrates has been well characterised in recent decades, of no less interest are the great strides that have also been made in characterizing and understanding the behavioural and cognitive basis of orientation and navigation in invertebrate models and in particular insects. Insects are known to exhibit remarkable spatial cognitive abilities and are able to successfully migrate over long distances or pinpoint known locations relying on multiple navigational strategies similar to those found in vertebrate models-all while operating under the constraint of relatively limited neural architectures. Insect orientation and navigation systems are often tailored to each species' ecology, yet common mechanistic principles can be observed repeatedly. Of these, reliance on visual cues is observed across a wide number of insect groups. In this review, we characterise some of the behavioural strategies used by insects to solve navigational problems, including orientation over short-distances, migratory heading maintenance over long distances, and homing behaviours to known locations. We describe behavioural research using examples from a few well-studied insect species to illustrate how visual cues are used in navigation and how they interact with non-visual cues and strategies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".