Seasonal Patterns in Ocean Ambient Noise near Sachs Harbour, Northwest Territories + Supplementary Appendix 1 (See Article Tools)
Bibliographic record
Abstract
Ocean ambient noise is a crucial habitat feature for marine animals because it represents the lower threshold of their acoustically active space. Ambient noise is affected by noise from both natural sources, like wind and ice, and anthropogenic sources, such as shipping and seismic surveys. During the ice-covered season, ambient conditions in the Arctic are quieter than those in other regions because sea ice has a dampening effect. Arctic warming induced by climate change can raise noise levels by reducing sea ice coverage and increasing human activity, and these changes may negatively affect several species of marine mammals and other acoustically sensitive marine fauna. We document ambient noise off the west coast of Banks Island near Sachs Harbour, Northwest Territories, to provide baseline noise levels for the eastern Beaufort Sea. Noise levels were comparable to those found in other studies of the Canadian Arctic and Alaska and were typically much lower than levels reported farther south. Stronger wind increased noise, whereas greater ice concentration decreased it, dampening the effect of wind speed. Future work should expand monitoring to other locations in the Arctic, model the impact of increased human activities on ambient noise levels, and predict the impact of these changing levels on marine animals.
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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 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.047 | 0.004 |
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".