Probabilistic Modelling of Soil Shear Strength by Maximum Entropy Quantile Functions
Bibliographic record
Abstract
The 1990 Nipigon River landslide that occurred north of the township of Nipigon, northwestern Ontario, is listed among the catastrophic landslides in Canada. Since 1990, many additional landslides of various scale and consequence have occurred in the Nipigon River area. To investigate the mechanism behind initiation of these landslides, a series of soil sampling and laboratory soil direct shear testing were conducted to measure the shear strength of watershed soils (cohesion and the angle of friction). Given that the shear strengths exhibit high variability (and hence uncertainty), they are amenable to a comprehensive probabilistic treatment. Conventionally, random variables are characterized using a probability density function or a cumulative distribution function; the type of function is usually determined from histograms and from the classical distributions such as normal and lognormal distributions, and the distribution parameters are estimated using the method of moments or the method of maximum likelihood. This paper proposes a novel probabilistic method to model soil properties using quantile functions, based on fractional probability-weighted moments, principle of maximum entropy, and Akaike information criterion. The quantile function is a counterpart to distribution functions of a random variable since the quantile function is mathematically the inverse cumulative distribution function. The maximum entropy method is presented to generate unbiased quantile functions for measured soil properties. The use of the fractional probability-weighted moments facilitates more accurate quantification of soil uncertainties by the entropy-based quantile functions than the probability density or cumulative distribution functions. Akaike information criterion is then used to locate the optimal order of maximum entropy quantile functions. Maximum entropy quantile distributions are compared to the traditional quantile distributions to evaluate their performance. The analytical entropy quantile distribution obtained can be used in probabilistic reliability analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".