Chemosensory afference in the tentacle nerve of <i>Lymnaea stagnalis</i>
Bibliographic record
Abstract
Abstract Although the neural control of behavior has been extensively studied in gastropods, basic gaps remain in our understanding of how sensory stimuli are processed. In particular, there is only patchy evidence regarding the functional roles of sense organs and the extensive peripheral nervous system they contain. Our goal was to use extracellular electrophysiological recordings to confirm the chemosensory role of the tentacles in the great pond snail, Lymnaea stagnali s. Employing a special twin channel suction electrode to improve signal-to-noise ratio, we applied three food odours (derived from earthworm-based food pellets, algae-based pellets, and fresh lettuce) to a reduced preparation of the tentacle while recording neuronal activity in the tentacle nerve. Responses were assessed by comparing average spike frequencies produced in response to saline flow with and without odours. Confirming the omnivorous nature of this gastropod, we report strong neuronal responses to earthworm-based food odours and mild neuronal responses to algae-based food odours. There were no clear neuronal responses produced when lettuce food odour or control saline was applied to the tentacle. Overall, our results provide strong evidence for the chemosensory role of the tentacles in L. stagnalis . While it is unclear whether the differences in neuronal responses can be explained by differing sizes, numbers, or populations of neurons, these results are a useful foundation for further study of peripheral nervous system function in gastropods. Summary Statement Great pond snail tentacles send sensory signals to the brain in response to some (but not all) food odours.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".