Temporary hearing threshold shift in harbor seals (<i>Phoca vitulina</i>) due to one-sixth-octave noise bands centered at 0.5, 1, and 2 kHz
Bibliographic record
Abstract
This study concludes a larger project on the frequency-dependent susceptibility to noise-induced temporary hearing threshold shift (TTS) in harbor seals (Phoca vitulina). Here, two seals were exposed to one-sixth-octave noise bands (NBs) centered at 0.5, 1, and 2 kHz at several sound exposure levels (SELs, in dB re 1 μPa2s). TTSs were quantified at the center frequency of each NB, half an octave above, and one octave above, at the earliest within 1–4 min after exposure. Generally, elicited TTSs were low, and the highest TTS1–4 occurred at half an octave above the center frequency of the fatiguing sound: after exposure to the 0.5-kHz NB at 210 dB SEL, the TTS1–4 at 0.71 kHz was 2.3 dB; after exposure to the 1-kHz NB at 207 dB SEL, the TTS1–4 at 1.4 kHz was 6.1 dB; and after exposure to the 2-kHz NB at 215 dB SEL, TTS1–4 at 2.8 kHz was 7.9 dB. Hearing always recovered within 60 min, and susceptibility to TTS was similar in both seals. The results show that, for the studied frequency range, the lower the center frequency of the fatiguing sound, the higher the SEL required to cause the same TTS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".