The under-ice soundscape in Great Slave Lake near the City of Yellowknife, Northwest Territories, Canada.
Bibliographic record
Abstract
The Department of Fisheries and Oceans and JASCO deployed an AMARs sound data recorder in Great Slave Lake near Yellowknife, Northwest Territories, between December 2009 and March 2010. One of the objectives of these recordings was to provide long-term ambient noise measurements in a large frozen lake near a major urban center. A recent study reported spot-measurements of under-ice noise in a subarctic lake from anthropogenic activity raising the sound levels up to 46 dB above ambient, and 10-h average ambient levels that were very low (spectral density levels of ∼45 dB re 1 μPa). The current Great Slave Lake data provide the opportunity to determine the long-term baseline of under-ice sound levels and how anthropogenic activity, such as air and ice-road traffic, adjacent to the study site affects the ambient levels and how these sounds may impact aquatic biota. Regressions of the ambient levels against wind speed and temperature are also discussed.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".