Vessel noise modelling around the Port of Prince Rupert, British Columbia, and the potential effects on marine mammal listening distance
Bibliographic record
Abstract
Underwater sound was modelled around the Port of Prince Rupert, British Columbia. The waters around the port region contain major international shipping lanes and important habitat for marine species, many of which require a quiet environment. Underwater noise calculations were carried out using JASCO's Acoustic Real-Time Exposure Model for In-motion Sources (ARTEMIS), a cumulative noise exposure and noise mapping model. Vessel traffic for three Periods were modelled using Automatic Identification System (AIS) vessel position reports from 2019: 1 to 31 January, 15 Jun to 15 July, and 15 September to 15 October. Vessel source levels were modelled using the JOMOPANS-ECHO reference spectrum model (MacGillivray and de Jong, 2021). Ambient noise was modelled using the Wind and Rain Ambient Sound Propagation (WRASP) model (Ainslie, 2010). Sound propagation loss was precomputed using the normal mode code, ORCA (Westwood et al., 1996). Vessel traffic projections for 2030 were created with AIS data from 2019 and 2020 to simulate underwater sound levels for 2030. Potential effects on marine mammals was assessed by calculating the relative reduction in distance (listening distance ratios) to which they could communicate, forage, and detect predators due to vessel noise. Listening distance ratios were assessed for 2030 using 2019 as a baseline.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".