Assessing Submarine Slope Stability through Deterministic and Probabilistic Approaches: A Case Study on the West-Central Scotia Slope
Bibliographic record
Abstract
A simplified geostatistical approach was adopted to assess the effect of spatial variability of soil properties on slope stability analysis in order to understand continental margin geologic processes and potential geohazards for an area of the central Scotian Slope, offshore Nova Scotia, Canada. The analyses are conducted on piston core samples, thus are restricted to ~12 m sub-seabed; however, the approach provides insight into the general effects of spatial and temporal variability. Data processing using geostatistics and assessment of spatial correlation are used to characterize the current dataset. A deterministic assessment was performed for both non-spatially averaged and spatially averaged core sections. The results indicate that the estimated factor of safety increased by about 30% when spatially averaged values were used. A probabilistic model is introduced to assess reliability of the slope. The approach makes use of estimates of both the mean and variance of input random variables (e.g., Su and γb). The model uses an exact probabilistic formulation for the total stress stability analysis and a Taylor series approximation for the effective stress stability analysis. In both cases, the mean and variance of the factor of safety are computed, leading to estimates of failure probability. The results suggest that the deterministic analysis is conservative with respect to slope reliability, although they do not lead to an estimate of the probability of failure. While these results indicate sediment instability is largely unlikely under static conditions, the reality is that many examples of submarine slope failure are observed in the geologic record. These results suggest that cyclic loading (earthquakes) or pre-conditioning factors (elevation of pore pressures) are critical for slope instability on the Scotian Slope.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".