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Record W1997639104 · doi:10.1121/1.4835835

Three-dimensional localization of transient acoustic sources using an ice-mounted geophone

2014· article· en· W1997639104 on OpenAlexaff
Stan E. Dosso

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsGeophoneGeologySea iceShear wavesAcousticsGeodesyPassive seismicSeismologyBearing (navigation)GeophysicsShear (geology)PhysicsClimatology

Abstract

fetched live from OpenAlex

This paper presents an approach to three-dimensional (3D) localization of ocean acoustic sources using a single three-component geophone on Arctic sea ice. Source bearing is estimated by maximizing the radial signal power as a function of horizontal look angle, applying seismic polarization filters to suppress shear waves with transverse particle motion. The inherent 180° ambiguity is resolved by requiring outgoing (prograde) particle motion in the radial-vertical plane. Source range and depth estimates and uncertainties are computed by Bayesian inversion of arrival-time differences of the water-borne acoustic wave and ice seismic waves, including the horizontally-polarized shear wave and longitudinal plate wave. The 3D localization is applied to geophone recordings of impulsive sources deployed in the water column at a series of ranges (200 to 1000 m) and bearings (0° to 90°) for three sites in the Lincoln Sea characterized by smooth annual ice, rough/ridged annual ice, and thick multi-year ice. Good bearing estimates are obtained in all cases. Range-depth localization is successful for ranges over which ice seismic arrivals could be reliably detected, approximately 200 m on rough ice, 500 m on smooth ice, and 800 m on multi-year ice. Effects of environmental uncertainty on localization are quantified by marginalizing over unknown environmental parameters.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.757
Threshold uncertainty score0.417

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.021
GPT teacher head0.260
Teacher spread0.238 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations8
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicUnderwater Acoustics ResearchFrench-language works237,207