Range versus frequency averaging of underwater propagation loss for soundscape modeling
Bibliographic record
Abstract
Guidance on efficient methods is needed for the practical application of modeling the sound field from broadband sources such as vessels, seismic surveys, and construction activities. These sound field models are employed for estimating how changes in the soundscape will affect marine life. For efficiency, acoustic propagation modeling is often performed in bands (decidecade or 13-octave), where propagation loss modeled for central frequency is assumed to represent an average propagation loss in the band. This shortcut comes at the expense of accuracy, which can be rectified by averaging the propagation loss across many frequencies in the band. Alternately, the equivalence of range and frequency averaging was shown by Harrison and Harrison [J. Acoust. Soc. Am. 97, 1314-1317 (1995)]. However, when and how to apply range averaging required further investigations. A simple environment with a flat sandy bottom and an isovelocity water-column sound speed profile was considered to test the agreement between the range and frequency averages for decidecade bands typically considered in soundscape modelling (10-1000 Hz). The optimal range smoothing window is a Gaussian window with a width of 10%-16% of the range from the source that switches to a width fixed beyond 20 km distance from the source.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".