Orcasound lab: A soundscape analysis case study in killer whale habitat with implications for coastal ocean observatories
Bibliographic record
Abstract
Orcasound lab is a cabled hydrophone array located near the shoreline of Haro Strait, the core summertime habitat of the endangered southern resident killer whales (SRKWs). In 2016–2017, we began to record data continuously on local hard drives and in 2018 are archiving both lossy and lossless data 24/7 in an AWS/S3 bucket. We discuss our statistical characterization of the soundscape from these continuous audio recordings, contextualized with the AIS data (to quantify sources of ship noise) and image data (to quantify sources of non-AIS boat noise). Of particular interest to ocean observatories are our methods of establishing non-anthropogenic acoustic baselines and then ranking noise pollution sources relative to these baselines. We explore the statistical consequences of selecting different averaging times (from seconds to years) and frequency band widths (spectrum to broadband levels) when computing baselines and pollution metrics, including “delta” metrics that may be most-relevant to SRKWs. Finally, we explain how soundscape analysis (with attention to tidal, diurnal, seasonal, or decadal time variations) could be implemented with cloud-based data in near-real-time and be enriched by citizen scientists interacting with a time-stamped live audio stream and other environmental data.
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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 | 0.002 | 0.007 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".