Regional soundscape modeling of the Atlantic Outer Continental Shelf
Bibliographic record
Abstract
The ocean soundscape is a complex superposition of sound from natural and anthropogenic sources. Recent advances in acoustic remote sensing and marine bioacoustics have highlighted how animals use their soundscape and how the background sound levels are influenced by human activities. In this paper, developments in computational ocean acoustics, remote sensing, and oceanographic modeling are combined to generate modelled sound fields at multiple scales in time and space. Source mechanisms include surface shipping, surface wind, and wave fields. A basin scale model is presented and applied to the United States Atlantic Outer Continental Shelf (OCS). For model-data comparison at a single hydrophone location, the model is run for a single receiver position. Environmental and source model uncertainty is included in the site-specific modeling of the soundscape. An inversion of the local sediment type is made for a set of sites in the OCS. After performing this inversion, the qualitative comparison of the modelled sound pressure level (SPL) time series and observed SPL is excellent. The quantitative differences in the mean root mean square error between the model and data is less than 3 dB for most sites and frequencies above 90 Hz.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".