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Record W4238436174 · doi:10.1121/2.0000030

A model for underwater sound levels generated by marine impact pile driving

2014· article· en· W4238436174 on OpenAlexaff
Alexander O. MacGillivray

Bibliographic record

VenueProceedings of meetings on acoustics · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsPileHammerUnderwaterAcousticsSuperposition principleSound pressureMechanicsGeotechnical engineeringGeologyPhysicsStructural engineeringEngineering

Abstract

fetched live from OpenAlex

Marine impact pile driving generates very high underwater sound pressures, which can harm aquatic life. Environmental assessments for pile driving projects typically require acoustic impact zones for marine mammals and fish to be estimated in advance. A computer model that predicts the radiated acoustic field from impact driving of cylindrical piles has been developed. A lumped-mass model of the hammer, which predicts the force generated at the top of the pile, is coupled to a 1-D finite-difference model of radial and axial stress waves in a cylindrical pile. The radiated pressure is computed by matching the velocity boundary condition at the pile wall using a superposition of monopole sources distributed over the length of the pile in a layered 2-D fluid medium. The transfer function for the monopoles is computed using the near-field Hankel transform for radial particle velocity at the pile wall. Standard ocean acoustic modeling techniques are used to compute the Mach wave propagating away from the pile. The model considers the physical characteristics of the pile-hammer system, such as the hammer energy, pile dimensions, sediment properties, and pile-hammer impedance ratio. The model's predictions are compared to field measurements obtained in a riverine environment.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.505
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.276
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2014
Admission routes1
Has abstractyes

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