Acoustic Signatures of Shipping, Weather and Marine Life: Comparison of NE Pacific and Arctic Soundscapes
Bibliographic record
Abstract
Acoustic signatures of shipping, weather and marine life are relatively well constrained, but there are strong variations with their oceanographic context and human activities. We investigate two contrasted settings, for timescales up to a year and frequencies up to 2 kHz. Arctic data from NOAA Noise Reference Station (NRS) NRS01, 500 m deep in the Arctic Chukchi Sea and away from major shipping areas is compared with measurements from Folger Deep, part of the Ocean Networks Canada network, 95 m deep and close to shipping lanes. PAMGuide is used to quantify broadband Sound Pressure Levels (SPLs), Third-Octave band Levels (TOLs), Power Spectral Densities (PSDs) and percentile contributions. The Acoustic Complexity Index (ACI) is an emerging metric to measure the apparent acoustic biodiversity, and we use its Seewave implementation. We compare the third-octave bands centred on 63 Hz and 125 Hz (“shipping” bands of the European Marine Strategy Framework Directive) in each environment and assess their use in the presence of heavy ice and little to no shipping. Metrics designed for open waters are not directly applicable to icy environments, or at least not on their own. They must be supplemented with multivariate analyses of context-specific third-octave bands.
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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 | 0.000 | 0.000 |
| 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".