Aerial audiograms of several California sea lions (<i>Zalophus californianus</i>) and Steller sea lions (<i>Eumetopias jubatus</i>) measured using single and multiple simultaneous auditory steady-state response methods
Bibliographic record
Abstract
Measurements of the electrophysiological auditory steady-state response (ASSR) have proven to be efficient for evaluating hearing sensitivity in odontocete cetaceans. In an effort to expand these methods to pinnipeds, ASSRs elicited by single and multiple simultaneous tones were used to measure aerial hearing thresholds in several California sea lions (Zalophus californianus) and Steller sea lions (Eumetopias jubatus). There were no significant differences between thresholds measured using the single and multiple ASSR methods, despite the more rapid nature of data collection using the multiple ASSR method. There was a high degree of variability in ASSR thresholds among subjects; thresholds covered a range of ∼40 dB at each tested frequency. As expected, ASSR thresholds were elevated relative to previously reported psychophysical thresholds for California and Steller sea lions. The features of high-frequency hearing limit and relative sensitivity of most ASSR audiograms were, however, similar to those of psychophysical audiograms, suggesting that ASSR methods can be used to improve understanding of hearing demographics in sea lions, especially with respect to high-frequency hearing. Thresholds for one Steller sea lion were substantially elevated relative to all other subjects, demonstrating that ASSR methods can be used to detect hearing loss in sea lions.
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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 | 0.000 | 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".