Masking release at 4 kHz in harbor porpoises (<i>Phocoena phocoena</i>) associated with sinusoidal amplitude-modulated masking noise
Bibliographic record
Abstract
Acoustic masking reduces the efficiency of communication, prey detection, and predator avoidance in marine mammals. Most underwater sounds fluctuate in amplitude. The ability of harbor porpoises (Phocoena phocoena) to detect sounds in amplitude-varying masking noise was examined. A psychophysical technique evaluated hearing thresholds of three harbor porpoises for 500-2000 ms tonal sweeps (3.9-4.1 kHz), presented concurrently with sinusoidal amplitude-modulated (SAM) or unmodulated Gaussian noise bands centered at 4 kHz. Masking was assessed in relation to signal duration and masker level, amplitude modulation rate (1, 2, 5, 10, 20, 40, 80, and 90 Hz), modulation depth (50%, 75%, and 100%) and bandwidth (1/3 or 1 octave). Masking release (MR) due to SAM was assessed by comparing thresholds in modulated and unmodulated maskers. Masked thresholds were affected by SAM rate with the lowest thresholds (i.e., largest MR was 14.5 dB) being observed for SAM rates between 1 and 5 Hz at higher masker levels. Increasing the signal duration from 500-2000 ms increased MR by 3.3 dB. Masker bandwidth and depth of modulation had no substantial effect on MR. The results are discussed with respect to MR resulting from envelope variation and the impact of noise in the environment.
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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".