Masking release at 4 and 32 kHz in harbor seals associated with sinusoidal amplitude-modulated masking noise
Bibliographic record
Abstract
Masking can reduce the efficiency of communication and prey and predator detection. Most underwater sounds fluctuate in amplitude, which may influence the amount of masking experienced by marine mammals. The hearing thresholds of two harbor seals for tonal sweeps (centered at 4 and 32 kHz) masked by sinusoidal amplitude modulated (SAM) Gaussian one-third octave noise bands centered around the narrow-band test sweep frequencies, were studied with a psychoacoustic technique. Masking was assessed in relation to signal duration, (500, 1000, and 2000 ms) and masker level, at eight amplitude modulation rates (1-90 Hz). Masking release (MR) due to SAM compared thresholds in modulated and unmodulated maskers. Unmodulated maskers resulted in critical ratios of 21 dB at 4 kHz and 31 dB at 32 kHz. Masked thresholds were similarly affected by SAM rate with the lowest thresholds and the largest MR being observed for SAM rates of 1 and 2 Hz at higher masker levels. MR was higher for 32-kHz maskers than for 4-kHz maskers. Increasing signal duration from 500 ms to 2000 ms had minimal effect on MR. The results are discussed with respect to MR resulting from envelope variation and the impact of noise in the environment on target signal detection.
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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) | 0.001 | 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".