Near-threshold equal-loudness contours for harbor seals (<i>Phoca vitulina</i>) derived from reaction times during underwater audiometry: A preliminary study
Bibliographic record
Abstract
Equal-loudness functions describe relationships between the frequencies of sounds and their perceived loudness. This pilot study investigated the possibility of deriving equal-loudness contours based on the assumption that sounds of equal perceived loudness elicit equal reaction times (RTs). During a psychoacoustic underwater hearing study, the responses of two young female harbor seals to tonal signals between 0.125 and 100 kHz were filmed. Frame-by-frame analysis was used to quantify RT (the time between the onset of the sound stimulus and the onset of movement of the seal away from the listening station). Near-threshold equal-latency contours, as surrogates for equal-loudness contours, were estimated from RT-level functions fitted to mean RT data. The closer the received sound pressure level was to the 50% detection hearing threshold, the more slowly the animals reacted to the signal (RT range: 188-982 ms). Equal-latency contours were calculated relative to the RTs shown by each seal at sound levels of 0, 10, and 20 dB above the detection threshold at 1 kHz. Fifty percent detection thresholds are obtained with well-trained subjects actively listening for faint familiar sounds. When calculating audibility ranges of sounds for harbor seals in nature, it may be appropriate to consider levels 20 dB above this threshold.
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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 | 0.001 | 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".