The summer soundscape of a shallow-water estuary used by beluga whales in the western Canadian Arctic
Bibliographic record
Abstract
The soundscape is an important habitat component for marine animals. In the Arctic, marine conditions are changing rapidly due to sea ice loss and increased anthropogenic activities such as shipping, which will influence the soundscape. Here, we assess the contributors to the summer soundscape in the shallow waters of the Mackenzie River estuary within the Tarium Niryutait Marine Protected Area in the western Canadian Arctic, a core summering habitat for beluga whales (Delphinapterus leucas Pallas, 1776). We collected passive acoustic data during the summer over four years, and assessed the influence of physical variables, beluga whale vocalizations, and boat noise on sound pressure levels in three frequency bands (low: 0.2–1 kHz; medium: 1–10 kHz; high: 10–48 kHz) to quantify the soundscape. Wind speed, wave height, beluga vocalizations, and boat noise were all large contributors to the soundscape in various frequency bands. The soundscape varied to a lesser degree between sites, time of day, and with tide height, but remained relatively constant between years. This study is the first detailed description of a shallow summer soundscape in the western Canadian Arctic, an important habitat for beluga whales, and can be used as a baseline to monitor future changes during this season.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".