Aerial audiograms of Steller and California sea lions measured using auditory steady-state response methods.
Bibliographic record
Abstract
Detection of aerial vocal signals by conspecifics is important in the reproductive behavior of the otariid pinnipeds. However, aerial hearing sensitivity measurements have only been obtained for a few otariid individuals that were trained to participate in behavioral experiments. In order to expand upon this small data set, auditory steady-state response (ASSR) methods were used to examine the aerial hearing sensitivity of Steller and California sea lions. Although ASSR thresholds were elevated relative to behavioral thresholds reported for otariids, the ASSR audiograms of the majority of individuals were similar to each other and to behavioral audiograms in terms of relative sensitivity. A marked reduction in sensitivity with increasing frequency regularly occurred between 16 and 32 kHz, indicating a consistent high-frequency cutoff. The reliability of the ASSR audiograms for both species suggests that behavioral aerial audiograms that exist for a few Steller and California sea lion individuals can be appropriately extrapolated to larger populations. The similarity of the ASSR audiograms among the Steller and California sea lions supports the notion that the otariid pinnipeds form a functional hearing group, with similar aerial hearing in terms of sensitivity and frequency range of hearing. [Work supported by ONR and NOAA Ocean Acoustics Program.]
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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) | 0.003 | 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".