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Record W7033284142

Pore Pressure Impact on Marine Mud Stability in Submarine Slumps in Offshore Grand Banks Canada

2022· dissertation· en· W7033284142 on OpenAlexaboutno aff

Bibliographic record

VenueThe Knowledge Bank (The Ohio State University) · 2022
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicPaleontology and Evolutionary Biology
Canadian institutionsnot available
Fundersnot available
KeywordsSlumpPore water pressureSubmarine landslideSubmarine pipelineSubmarineHydrostatic pressureLandslideOverpressureTurbidity current
DOInot available

Abstract

fetched live from OpenAlex

The 7.2 magnitude earthquake off the coast of Grand Banks Newfoundland in 1929 caused a massive underwater landslide and deadly tsunami, one of the largest recorded in Canadian history. The landslide resulted from the failing of a submarine slump which was jarred loose by the earthquake, which displaced a massive volume of sediment and turbidity currents which in turn created a tsunami. Using past research and collected data such as seismic cross sections, as well as reasonable assumptions for data which must be inferred, this study aims to find stress values at different depths that would cause failure of a nearby slump while factoring in pore pressure. This method uses Mohr-Coulomb circles to calculate required pore pressure increases from hydrostatic pressure to cause failure in a modern day slump near the 1929 epicenter. These findings determine that the slump studied is relatively stable when the pore pressure is assumed to be hydrostatic and for most of the six scenarios analyzed. Found pore pressure increases needed were ~3.5 MPa to ~10 MPa for the weakest scenarios for the slump, and increases of ~8 MPa to ~22 MPa for the stronger scenarios to cause their Mohr-Circles to contact the failure envelope. These findings and methods can be used for slumps in the surrounding area and for other locations around the world near passive margins with submarine slumps.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.522
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.204
Teacher spread0.195 · 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.

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

Citations0
Published2022
Admission routes1
Has abstractyes

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