Determining the dependence of marine pile driving sound levels on strike energy, pile penetration, and propagation effects using a linear mixed model based on damped cylindrical spreading
Bibliographic record
Abstract
Acoustic recordings were made during the installation of four offshore wind turbines at the Block Island Wind Farm, Rhode Island, USA. The turbine foundations have four legs inclined inward in a pyramidal configuration. Four bottom mounted acoustic recorders measured received sound levels at distances of 541-9067 m during 24 pile driving events. Linear mixed models based on damped cylindrical spreading were used to analyze the data. The model's random effects coefficients represented useful information about variability in the acoustic propagation conditions. The received sound levels were dependent on the angle between pile and seabed, strike energy, and pile penetration (PP). Deeper PPs increased sound levels in a frequency dependent manner. The estimated area around the piles where auditory injury and disturbance to marine life could occur were not circular and changed by up to an order of magnitude between the lowest and highest sound level cases. The study extends earlier results showing a linear relationship between the peak sound pressure level and per-strike sound exposure level. Recommendations are made for how to collect and analyze pile driving data. The results will inform regulatory mitigations of the effects of pile driving sound on marine life, and contribute to developing improved pile driving source models.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".