Data-centric quasi-site-specific prediction for compressibility of clays
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
A generic clay database consisting of six parameters, including compression index ( C c ) and unloading–reloading index ( C ur ), is compiled from 429 studies. This database, labeled as CLAY-C c /6/6203, contains 6203 records. A data-driven approach of predicting C c and C ur for a target site by combining sparse site-specific data with CLAY-C c /6/6203 is illustrated. This data-driven approach consists of two steps. The first step is a learning step that adopts a hierarchical Bayesian model (HBM) to learn the prior information in CLAY-C c /6/6203 (both inter-site and intra-site variabilities). The second step is a Bayesian inference step that updates the prior model into a posterior model. The inference outcome is a quasi-site-specific model. A real case study (Baytown, Texas, USA) is adopted to illustrate the application of the HBM-MUSIC-3X method in estimating and simulating the 3D spatially varying C c and C ur profiles. The key conclusions are as follows: ( i) predictions from Big Indirect Data (BID) in the form of CLAY-C c /6/6203 can be biased with large transformation uncertainty although data are abundant, ( ii) predictions from small (sparse) site-specific data are less biased but suffer from high statistical uncertainty although data are directly applicable, and ( iii) combining BID and site-specific data using an HBM learning strategy that accounts for site uniqueness is effective in terms of reducing prediction uncertainty.
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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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