Bayesian identification of soil stratigraphy based on soil behaviour type index
Why this work is in the frame
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Bibliographic record
Abstract
The cone penetration test (CPT) has been widely used to determine the soil stratigraphy (including the number N and thicknesses H N of soil layers) during geotechnical site investigation because it is rapid, repeatable, and economical. For this purpose, several deterministic and probabilistic approaches have been developed in the literature, but these approaches generally only give the “best” estimates (e.g., the most probable values) of N and H N based on CPT data according to prescribed soil stratification criteria, providing no information on the identification uncertainty (degrees-of-belief) in these “best” estimates. This paper develops a Bayesian framework for probabilistic soil stratification based on the profile of soil behaviour type index I c calculated from CPT data. The proposed Bayesian framework not only provides the most probable values of N and H N , but also quantifies their associated identification uncertainty based on the I c profile and prior knowledge. Equations are derived for the proposed approach, and they are illustrated and validated using real and simulated I c profiles. Results show that the proposed approach properly identifies the most probable soil stratigraphy based on the I c profile and prior knowledge, and rationally quantifies the uncertainty in identified soil stratigraphy with consideration of inherent spatial variability of I c .
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it