Pressure and particle motion detection thresholds in fish: a re-examination of salient auditory cues in teleosts
Bibliographic record
Abstract
The auditory evoked potential technique has been used for the past 30 years to evaluate the hearing ability of fish. The resulting audiograms are typically presented in terms of sound pressure (dB re. 1 μPa) with the particle motion (dB re. 1 m s(-2)) component largely ignored until recently. When audiograms have been presented in terms of particle acceleration, one of two approaches has been used for stimulus characterisation: measuring the pressure gradient between two hydrophones or using accelerometers. With rare exceptions these values are presented from experiments using a speaker as the stimulus, thus making it impossible to truly separate the contribution of direct particle motion and pressure detection in the response. Here, we compared the particle acceleration and pressure auditory thresholds of three species of fish with differing hearing specialisations, goldfish (Carassius auratus, weberian ossicles), bigeye (Pempheris adspersus, ligamentous hearing specialisation) and a third species with no swim bladder, the common triplefin (Forstergyian lappillum), using three different methods of determining particle acceleration. In terms of particle acceleration, all three fish species have similar hearing thresholds, but when expressed as pressure thresholds goldfish are the most sensitive, followed by bigeye, with triplefin the least sensitive. It is suggested here that all fish have a similar ability to detect the particle motion component of the sound field and it is their ability to transduce the pressure component of the sound field to the inner ear via ancillary hearing structures that provides the differences in hearing ability. Therefore, care is needed in stimuli presentation and measurement when determining hearing ability of fish and when interpreting comparative hearing abilities between species.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".