Shipping noise in whale habitat: Characteristics, sources, budget, and impact on belugas in Saguenay–St. Lawrence Marine Park hub
Bibliographic record
Abstract
A continuous car ferry line crossing the Saguenay Fjord mouth and traffic from the local whale-watching fleet introduce high levels of shipping noise in the heart of the Saguenay-St. Lawrence Marine Park. To characterize this noise and examine its potential impact on belugas, a 4-hydrophone array was deployed in the area and continuously recorded for five weeks in May-June 2009. The source levels of the different vessel types showed little dependence on vessel size or speed increase. Their spectral range covered 33 dB. Lowest noise levels occurred at night, when ferry crossing pace was reduced, and daytime noise peaked during whale-watching tour departures and arrivals. Natural ambient noise prevailed 9.4% of the time. Ferry traffic added 30-35 dB to ambient levels above 1 kHz during crossings, which contributed 8 to 14 dB to hourly averages. The whale-watching fleet added up to 5.6 dB during peak hours. Assuming no behavioral or auditory compensation, half of the time, beluga potential communication range was reduced to less than ~30% of its expected value under natural noise conditions, and to less than ~15% for one quarter of the time, with little dependence on call frequency. The echolocation band for this population of belugas was also affected by the shipping noise.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".