Probabilistic quasi-site-specific CPT-based soil classification
Bibliographic record
Abstract
The current study compiles a database named CPT-USCS/3/2017 that consists of 2017 pairwise cone penetration test (CPT) versus Unified Soil Classification System (USCS) category data from 228 global sites. The current study also proposes a novel hierarchical Bayesian model (HBM) framework named USCS-HBM to learn the inter-site and intra-site characteristics in the database. The USCS-HBM trained by the database can produce a prior model for the target site, and this prior model is updated by the sparse target-site data into the quasi-site-specific model. The resulting quasi-site-specific model can be adopted to predict USCS categories based on CPT measurements. The proposed USCS-HBM framework explicitly addresses the challenge of site uniqueness in CPT-based soil classification as well as the practical challenge of sparse target-site data. Case studies and extensive cross-validations showed that the proposed USCS-HBM framework can provide meaningful prediction results for USCS categories based on CPT measurements even if the target-site data are sparse.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".