One rhinophore likely provides sufficient sensory input for odour-based navigation by the nudibranch mollusc,<i>Tritonia diomedea</i>
Bibliographic record
Abstract
Tritonia diomedea (synonymous with Tritonia tetraquetra) navigates in turbulent odour plumes, crawling upstream towards prey and downstream to avoid predators. This is probably accomplished by odour-gated rheotaxis, but other possibilities have not been excluded. Our goal was to test whether T. diomedea uses odour-gated rheotaxis and to simultaneously determine which of the cephalic sensory organs (rhinophores and oral veil) are required for navigation. In a first experiment, slugs showed no coherent responses to streams of odour directed at single rhinophores. In a second experiment, navigation in prey and predator odour plumes was compared between animals with unilateral rhinophore lesions, denervated oral veils, or combined unilateral rhinophore lesions and denervated oral veils. In all treatments, animals navigated in a similar manner to that of control and sham-operated animals, indicating that a single rhinophore provides sufficient sensory input for navigation (assuming that a distributed flow measurement system would also be affected by the denervations). Amongst various potential navigational strategies, only odour-gated positive rheotaxis can produce the navigation tracks we observed in prey plumes while receiving input from a single sensor. Thus, we provide strong evidence that T. diomedea uses odour-gated rheotaxis in attractive odour plumes, with odours and flow detected by the rhinophores. In predator plumes, slugs turned downstream to varying degrees rather than orienting directly downstream for crawling, resulting in greater dispersion for negative rheotaxis in aversive plumes. These conclusions are the first explicit confirmation of odour-gated rheotaxis as a navigational strategy in gastropods and are also a foundation for exploring the neural circuits that mediate odour-gated rheotaxis.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".