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Record W2026774428 · doi:10.1121/1.3249553

Estimating geoacoustic properties of marine sediment on the New Jersey Continental Shelf from broadband signals.

2009· article· en· W2026774428 on OpenAlexaff
Yong‐Min Jiang, N. Ross Chapman

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsGeologyBroadbandWaves and shallow waterRidgeContinental shelfSedimentOceanographySpeed of soundInversion (geology)AcousticsWater columnSeabedFrequency bandSeismologyGeomorphologyTelecommunicationsPaleontologyBandwidth (computing)OpticsPhysics

Abstract

fetched live from OpenAlex

This paper presents geoacoustic inversions of broadband signals collected in the Shallow Water 2006 Experiments off the coast of New Jersey. An L-shaped array was deployed on the top of a sand ridge, in 70 m of water. The acoustic source was maintained at a distance of 190 m from the vertical leg of the L-shape array, and lowered from 10 to 60 m in 10-m intervals in the experiment. Two sets of chirps, low frequency (100–900 Hz) and midfrequency (1100–2900 Hz), were transmitted with approximately the same experimental geometry. The water column sound speed profiles were measured at the source position. This study examines the sediment information content from different frequency bands recorded on the vertical leg of the L-shape array. Because the chirps have different temporal resolutions and energy penetrabilities in the sediment, the received signals exhibit different bottom structures at different frequency bands. Travel time geoacoustic inversions are carried out at both frequency bands using signals from the resolvable reflections from the sediment layers. The variation in the oceanic sound speed profile is parametrized in terms of its characteristics and included in the inversion. [Work supported by ONR Ocean Acoustics.]

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: Empirical
Teacher disagreement score0.368
Threshold uncertainty score0.979

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.237
Teacher spread0.216 · 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

Citations2
Published2009
Admission routes1
Has abstractyes

Explore more

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