Partitioning wind and ship generated sound using vertical noise coherence
Bibliographic record
Abstract
Continuous ambient noise data were recorded during April and May 2016, using a four-element vertical array deployed near the continental shelf break south of Martha’s Vineyard. A technique for classifying and partitioning ambient noise using the vertical noise coherence function is proposed. Time series analysis of the noise power spectrum reveals the presence wind, distant shipping, and near-field individual ship noise in the region. The noise coherence (directionality) due to wind, distant shipping, and individual ships is analytically modeled using environmental inputs such as the time varying sound speed profile and sediment properties from the measurement site, and compared with the observation. The impact of noise due to ship traffic in the region is estimated by subtracting the best-fit theoretical coherence for wind-generated noise from the measurement. Since the wind generated vertical coherence is stable and independent of source spectrum level, it can be used to quantify the relative contributions of distant shipping and wind noise to the marine environment. Additionally, the time varying vertical coherence from near-field individual ships can be used for estimating their range and speed. [Research supported by ONR.]
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".