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Record W3194476665 · doi:10.1029/2021gc009720

Shear‐Wave Velocity Structure of Sediments on Cascadia's Continental Margin From Probabilistic Inversion of Seafloor Compliance Data

2021· article· en· W3194476665 on OpenAlexaff
Stephen Mosher, Pascal Audet, Jeremy M. Gosselin

Bibliographic record

VenueGeochemistry Geophysics Geosystems · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsGeologySeismometerContinental shelfSeismologySeafloor spreadingContinental marginSubductionSubmarine pipelineBathymetryBroadbandInversion (geology)OceanographyTectonics

Abstract

fetched live from OpenAlex

Abstract Several seismic techniques, both passive and active, exist for estimating the shear‐wave velocity structure of shallow sedimentary structures. In particular, passive compliance signals recorded by broadband ocean‐bottom seismometers (OBSs) can be used to invert for structure. While compliance‐based imaging studies have been carried out at several locations across the Cascadia Subduction Zone, such an approach has not been extensively applied to OBSs deployed on the continental shelf and slope. In this study, we measure compliance and coherence signals at 13 broadband OBSs deployed along Cascadia's continental shelf and upper slope. We then use a recently developed technique to probabilistically invert compliance signals for shallow structure that makes use of mixture density neural networks. Finally, we compare and contrast our inverted profiles and derived properties obtained using this method with previous studies focused on the properties of basin sediments.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.031
GPT teacher head0.229
Teacher spread0.199 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations6
Published2021
Admission routes1
Has abstractyes

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