Pore Pressure Impact on Marine Mud Stability in Submarine Slumps in Offshore Grand Banks Canada
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".