The under-ice soundscape in Great Slave Lake near the city of Yellowknife, Northwest Territories, Canada
Bibliographic record
Abstract
Most recent research and monitoring of under-water “soundscapes” has focused on marine systems in open water conditions. Here we present the first long-term assessment of the diel and seasonal patterns of a fresh-water aquatic soundscape under-ice cover. Acoustic data recorded in Yellowknife Bay, Great Slave Lake in Canada's Northwest Territories, measured the under-ice soundscape near an ice road and airport. From December to late January, the soundscape consisted of geophony from ice cracking and anthrophony from snowmobiles, aircraft, and road vehicles. In late January, burbot spawning calls began and added a localized biophony source to the soundscape that increased the total sound pressure level due to an increase in sound levels in the 10–425 Hz frequency band. The median 1 min root-mean-square sound pressure level (rms SPL) in the period without burbot biophony was 90.3 dB re 1 μPa. The measured hourly rms SPL was negatively correlated with air temperature in the 200–800 Hz band but positively correlated with average hourly wind speed in the 800–8000 Hz band. The nightly mean rms SPL was 88 dB re 1 μPa and increased to 96 dB re 1 μPa in late afternoon. This diel cycle had a strong positive correlation with the number of minutes per hour where ice-road vehicles were detected. Further work is recommended to quantify the soundscape in deep-water areas of large lakes and to include particle motion. Such information will enable the assessment of cumulative impacts of anthrophony and geophony on aquatic biota.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".