Characteristic Strength of a Slope with Spatial Variability and Cross-Correlation
Bibliographic record
Abstract
A methodology is presented for selecting input strength parameters for deterministic c-ϕ slope stability analyses to account for spatial variability and cross-correlation of the material properties. It can be non-conservative to use mean values of the properties in slope stability analysis since the critical failure path will preferentially develop through the weakest materials. For materials exhibiting spatial variability, the average strength along the critical surface will be less than the mean strength, so the mean factor of safety from a probabilistic analysis will be less than the deterministic value. Recent publications have emphasized the importance of considering spatial variability and cross-correlation to account for these effects. However, not every project has sufficient data or means to conduct rigorous probabilistic analyses. The proposed method adjusts the c-ϕ parameters to account for spatial variability and cross-correlation. The result is a deterministic factor of safety matching the mean value from a probabilistic analysis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".