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Record W2087990549 · doi:10.1121/1.3311822

Estimation of seismic velocities of upper oceanic crust from ocean bottom reflection loss data

2010· article· en· W2087990549 on OpenAlexaff
Hefeng Dong, N. Ross Chapman, David Hannay, Stan E. Dosso

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsGeologyOcean bottomOceanic crustReflection (computer programming)SeismologyCrustSeabedGeophysicsGeodesyOceanographySubduction

Abstract

fetched live from OpenAlex

This paper describes a Bayesian inversion of acoustic reflection loss versus angle measurements to estimate the compressional and shear wave velocities in young uppermost oceanic crust, Layer 2A. The data were obtained in an experiment on the thinly sedimented western flank of the Endeavor segment of the Juan de Fuca Ridge, using a towed horizontal hydrophone array and small explosive charges as sound sources. Measurements were made at three sites at increasing distance from the ridge spreading center to determine the effect of age of the crust on seismic velocities. The inversion used reflection loss data in a 1/3-octave band centered at 16 Hz. The compressional and shear wave velocities of the basalt were highly sensitive parameters in the inversion. The compressional wave velocity increased from 2547 + or - 30 to 2710 + or - 18 m/s over an age span of 1.4 million years (Ma) from the spreading center, an increase of 4.5 + or - 1.0%/Ma. The basalt shear wave velocity increased by nearly a factor of 2, from approximately 725 to 1320 m/s over the same age span. These results show a decreasing trend of Poisson's ratio with age, from a value of 0.46 at the youngest site closest to the ridge axis.

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 score0.334
Threshold uncertainty score0.476

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)0.0000.000

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 head0.026
GPT teacher head0.280
Teacher spread0.254 · 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

Citations7
Published2010
Admission routes1
Has abstractyes

Explore more

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