Arctic ambient noise statistics at the edge of the Canada Basin
Bibliographic record
Abstract
Knowledge of ambient noise is important for the design and operation of acoustic observation and communication systems. Several concurrent year-long acoustic datasets from the Canada Basin Acoustic Propagation Experiment (CANAPE) were collected in 2016–2017. Selected data were analyzed to investigate soundscape temporal and spatial characteristics in an area on the western slope of the Chukchi shelf and Canada Basin, north of Barrow Alaska. From October 2016 to October 2017, four 8-element vertical arrays were deployed in water depths between 100 and 300 m. Each array recorded at approximately 15% duty cycle at several sample rates between 4000 Hz and 64000 Hz and at 24-bit resolution. One-minute, 10-second, and 1-second root-mean-square sound pressure levels were computed to generate ambient noise statistics and summarized in empirical probability density functions (PDF) for various bands. Fit functions for ambient noise were determined for each empirical PDF. Impulse detection was used to investigate potential correlation of ice cracking with ambient noise. A multivariate correlation of ambient noise levels was performed with several environmental parameter covariates, including ice cover, wind speed, and air temperature. These correlations may form the basis for predictive models for ambient noise modeling in the arctic.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".