Regional slope stability assessment: Challenges in spatial and stratigraphic geologic and geotechnical data integration
Bibliographic record
Abstract
Recent oil and gas exploration in Eastern Canada includes deep water continental slope regions. Sediment instability and risk of submarine mass failure is the most significant geohazard in this environment, as demonstrated in the rock record and even in historic times with the 1929 Grand Bank's landslide. An abundance of seismic reflection data and numerous piston cores along the Nova Scotia continental margin make it an ideal area to perform a regional slope stability assessment. Site-specific assessments typically involve slope stability analysis to predict static and dynamic critical slope failure conditions. Vertical measurements of sediment geotechnical properties used in these analyses can be reasonably extrapolated on local scales for site assessment purposes. Regional slope stability assessments, however, have the challenge of integrating geological and geotechnical conditions that vary spatially and stratigraphically. In this study, a simplified geostatistical approach was adopted to assess the effect of spatial variability of soil properties on slope stability analysis. Probabilistic and deterministic engineering assessments were performed for both non-spatially averaged and spatially averaged core sections. Results indicate that the estimated factor of safety increased by 30% when spatially averaged values were used. A slope of 10o has a 50% probability of failure under static conditions. The average slope angle for the area is between 1 and 3o. In this case, a seismic coefficient of ~12% is required to initiate instability. Given the abundance of mass transport deposits in the stratigraphic section, occasional strong earthquakes to generate these coefficients must have occurred in the past. Other contributive factors may have resulted in weakening of sediment in the stratigraphic section to lessen these critical coefficients.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".