Bibliographic record
Abstract
Gastropod diversity is substantial in marine and freshwater habitats, and many aquatic slugs and snails use olfactory cues to guide their navigation behaviour. Examples include finding prey or avoiding predators based on kairomones, or finding potential mates using pheromones. Here, I review the diversity of navigational behaviours studied across the major aquatic taxa of gastropods. I then synthesize evidence for the different theoretical navigation strategies the animals may use. It is likely that gastropods regularly use either chemotaxis or odour-gated rheotaxis (or both) during olfactory-based navigation. Finally, I collate the patchwork of research conducted on relevant proximate mechanisms that could produce navigation behaviours. Although the tractability of several gastropod species for neurophysiological experimentation has generated some valuable insight into how turning behaviour is triggered by contact chemoreception, there remain many substantial gaps in our understanding for how navigation relative to more distant odour sources is controlled in gastropods. These gaps include little information on the chemoreceptors and mechanoreceptors (for detecting flow) found in the peripheral nervous system and the central (or peripheral) processing circuits that integrate that sensory input. In contrast, past studies do provide information on motor neurons that control the effectors that produce crawling (both forward locomotion and turning). Thus, there is plenty of scope for further research on olfactory-based navigation, exploiting the tractability of gastropods for neuroethology to better understand how the nervous system processes chemosensory input to generate movement towards or away from distant odour sources.
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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) | 0.000 | 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".