Probabilistic quasi-site-specific CPT-based soil classification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 |
| 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