Development of ultrasound detection in American shad (<i>Alosa sapidissima</i>)
Bibliographic record
Abstract
It has recently been shown that a few fish species, including American shad (Alosa sapidissima; Clupeiformes), are able to detect sound up to 180 kHz, an ability not found in most other fishes. Initially, it was proposed that ultrasound detection in shad involves the auditory bullae, swim bladder extensions found in all members of the Clupeiformes. However, while all clupeiformes have bullae, not all can detect ultrasound. Thus, the bullae alone are not sufficient to explain ultrasound detection. In this study, we used a developmental approach to determine when ultrasound detection begins and how the ability to detect ultrasound changes with ontogeny in American shad. We then compared changes in auditory function with morphological development to identify structures that are potentially responsible for ultrasound detection. We found that the auditory bullae and all three auditory end organs are present well before fish show ultrasound detection behaviourally and we suggest that an additional specialization in the utricle (one of the auditory end organs) forms coincident with the onset of ultrasound detection. We further show that this utricular specialization is found in two clupeiform species that can detect ultrasound but not in two clupeiform species not capable of ultrasound detection. Thus, it appears that ultrasound-detecting clupeiformes have undergone structural modification of the utricle that allows detection of ultrasonic stimulation.
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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 | 0.001 | 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".