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Record W1994789837 · doi:10.1130/g32686.1

Gas domes in soft cohesive sediments

2012· article· en· W1994789837 on OpenAlexaff
Mark A. Barry, Bernard P. Boudreau, Bruce D. Johnson

Bibliographic record

VenueGeology · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsDalhousie University
FundersOffice of Naval Research
KeywordsGeologyGeochemistryEarth scienceGeomorphologyMining engineeringGeotechnical engineering

Abstract

fetched live from OpenAlex

Accumulation of gaseous methane can generate seabed domes in soft cohesive sediments. Such structures can, in turn, lead to seafloor instability, act as precursors to pockmark formation, and arguably pose a threat to seafloor drilling if they contain significantly overpressured gas. Future melting of gas hydrates within the seabed, due to global warming, will likely lead to a significant long-term release of methane, which could potentially produce a new and abundant generation of gas domes and associated pockmarks. Despite their geological and practical significance, our understanding of gas-dome formation in marine sediments has been limited to observations and qualitative analyses. To provide a quantitative understanding, we conducted small-scale laboratory doming experiments. We found that thin layers of clayey sediment behave elastically over the range of deformations needed to create seabed domes. The observed behavior is well described by elastic thin-plate mechanics, from which it is possible to predict the gas pressure required to create natural domes. Our results suggest that observed shallow dome geometries require surprisingly small overpressures to form; however, large overpressures can build under increasingly thicker and stiffer layers of sediment.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0170.007

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 head0.009
GPT teacher head0.224
Teacher spread0.215 · 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; both teacher heads agree on what is shown here.

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

Citations39
Published2012
Admission routes1
Has abstractyes

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