High-Speed Vessel Noises in West Hong Kong Waters and Their Contributions Relative to Indo-Pacific Humpback Dolphins (<i>Sousa chinensis</i>)
Bibliographic record
Abstract
The waters of West Hong Kong are home to a population of Indo-Pacific humpback dolphins (Sousa chinensis) that use a variety of sounds to communicate. This area is also dominated by intense vessel traffic that is believed to be behaviorally and acoustically disruptive to dolphins. While behavioral changes have been documented, acoustic disturbance has yet to be shown. We compared the relative sound contributions of various high-speed vessels to nearby ambient noise and dolphin social sounds. Ambient noise levels were also compared between areas of high and low traffic. We found large differences in sound pressure levels between high traffic and no traffic areas, suggesting that vessels are the main contributors to these discrepancies. Vessel sounds were well within the audible range of dolphins, with sounds from 315–45,000 Hz. Additionally, vessel sounds at distances ≥100 m exceeded those of dolphin sounds at closer distances. Our results reaffirm earlier studies that vessels have large sound contributions to dolphin habitats, and we suspect that they may be inducing masking effects of dolphin sounds at close distances. Further research on dolphin behavior and acoustics in relation to vessels is needed to clarify impacts.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".