A comparison of probabilistic distributions of undrained shear strength of soils in Nipigon River, Canada
Bibliographic record
Abstract
Abstract In probabilistic reliability analysis and design, critical geotechnical variables such as soil shear strength are usually regarded as random variables with a probability distribution rather than deterministic values or constants. In this paper, the vane shear test is briefly introduced and used to obtain undrained shear strength of soil in the area of Nipigon river landslide, Ontario, Canada. Then the maximum entropy method is presented to generate an unbiased probabilistic distribution for soil properties based on optimal-order moments from observed soil samples. A comparative study between maximum entropy distributions and traditional lognormal and normal distributions is conducted to evaluate the performance of fitted probabilistic distributions. Kolmogorov-Smirnov goodness of fit test shows that the maximum entropy distribution with four order moments fit the undrained shear strength best. The analytical entropy distribution obtained can be used in probabilistic reliability 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".