Ultrasonic hearing abilities of the domestic cat assessed with auditory brainstem responses
Bibliographic record
Abstract
Domestic cats (Felis catus) have sharp sensory abilities which they use in various circumstances. For example, they have acute hearing for detecting and localizing relevant auditory information, such as the presence of potential threats or prey items. However, there are notable discrepancies in the literature regarding the full extent of the cat’s hearing abilities. Here, we hypothesize that domestic cats can hear ultrasonic frequencies above 60kHz, since they might utilize their hearing abilities to detect the ultrasonic vocalizations emitted by rodent prey. We used auditory brainstem responses (ABRs), a more time efficient method compared to behavioral psychoacoustic techniques, to evaluate the sensitivity of the cat’s auditory system to ultrasonic frequencies. We presented artificial and behaviorally relevant stimuli containing ultrasonic frequencies to each cat (n = 6). We then recorded the resulting ABRs, measured the wave amplitudes and latencies, and determined the ABR thresholds to these stimuli. The ABR data presented here will be useful in conjunction with psychoacoustic experiments to provide insight into the neural mechanisms that might be involved when cats perceive high frequency signals. This work will ultimately contribute to a better understanding of the cat’s hearing abilities. [Work supported by the Natural Sciences and Engineering Research Council of Canada.]
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".