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Record W4254346868 · doi:10.1306/13201147m893344

Monitoring Sea-floor Instability Caused by the Presence of Gas Hydrate Using Ocean Acoustical and Geophysical Techniques in the Northern Gulf of Mexico

2009· book-chapter· en· W4254346868 on OpenAlexaff
Erika Geresi, Ross Chapman, Tom McGee, Bob Woolsey

Bibliographic record

VenueAmerican Association of Petroleum Geologists eBooks · 2009
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMemoirClathrate hydrateGeologyEarth scienceArchaeologyHistoryArt historyHydrateChemistry

Abstract

fetched live from OpenAlex

Abstract The northern Gulf of Mexico is characterized by an extremely heterogeneous near-surface geology that makes the geophysical identification of subsurface gas hydrate challenging, and the interpretation of data is commonly ambiguous. This chapter describes a set of novel seismic experiments designed to characterize the subsurface hydrate distribution at Mississippi Canyon Block 798 (MC798) in the northern Gulf of Mexico. A vertical line array (VLA), specially designed for high resolution in shallow sediments, was deployed by the Center for Marine Resources and Environmental Technology (CMRET) with the general objectives of (1) acquiring very high-resolution seismic reflection profiles in 850 m (2789 ft) of water depth, (2) studying the acoustic character and features of the sea floor for evidence of sea-floor hazards, and (3) looking for evidence of subsurface gas hydrates and their properties. Comparisons were made of the results of several seismic experiments in the area of interest in MC798, including the prototype VLA test in 2003, and conventional multi- and single-channel seismic data from previous years. In this article, these data sets were integrated to show the improved resolution of the near-surface sediments in the VLA data. Two interpretations of the geology are given; the evidence for the presence of subsurface gas hydrate is ambiguous.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score0.716

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.010
GPT teacher head0.231
Teacher spread0.221 · 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 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

Citations3
Published2009
Admission routes1
Has abstractyes

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