The contribution of the lateral line to 'hearing' in fish
Bibliographic record
Abstract
In the underwater environment, sound propagates both as a pressure wave and as particle displacement, with particle displacement dominating close to the source (the nearfield). At the receptor level, both the fish ear and the neuromast hair cells act as displacement detectors and both are potentially stimulated by the particle motion component of sound sources, especially in the nearfield. A now common way to test 'hearing' in fish involves auditory evoked potentials (AEPs), with recordings made from electrodes implanted near the auditory brainstem. These AEP recordings are typically conducted in enclosed acoustic environments with the fish well within the nearfield, especially for lower frequencies. We tested the contribution of neuromast hair cells to AEP by first testing intact goldfish (Carassius auratus), then ablating their neuromasts with streptomycin sulphate--disabling superficial and canal neuromasts--and retesting the same goldfish. We performed a similar experiment where only the superficial neuromasts were physically ablated. At 100 and 200 Hz, there was a 10-15 dB increase in threshold after streptomycin treatment but no significant difference at higher frequencies. There was no difference in threshold in control fish or in fish that only had superficial neuromasts removed, indicating that the differential responses were driven by canal neuromasts. Taken together, these results indicate that AEP results at lower frequencies should be interpreted as multimodal responses, rather than as 'hearing'. The results also suggest that in natural situations both the ear and lateral line likely play an integrative role in detecting and localising many types of 'acoustic' stimuli.
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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 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".