Underwater noise produced by anthropogenic activities on Vancouver Island, British Columbia
Bibliographic record
Abstract
In recent decades shipping traffic has been increasing, leading to elevated ambient underwater noise. Extensive research has been conducted on the changes to ambient noise levels of moving ships, but little is known for ships at anchor. Vancouver Island, British Columbia has many anchorage locations where freighters stop prior to entering the Port of Vancouver. Additionally, Vancouver Island has logging activities that occur in estuaries and surrounding waters. These human activities raise concern about what impacts they might be having on the soundscape and marine organisms that inhabit these key locations. Cowichan Bay, BC is an industrialized bay and a key migration corridor for Pacific Salmon (Oncorhynchus spp.). To understand changes to the ambient noise levels in Cowichan Bay, with different anthropogenic activities, seven stationary hydrophones were deployed during Fall 2019 and 2020. Results show substantial changes in the soundscape with both anchored freighters and logging activities for the duration of their presence in the bay, with elevated SPL detected throughout the bay for anchored freighters. Our results demonstrate the impact anchored freighters and logging activities have on underwater soundscapes and are the first step in understanding the impact these activities have on marine organisms and important ecosystems.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".