Interpolating spatially varying soil property values from sparse data for facilitating characteristic value selection
Bibliographic record
Abstract
Limit state design, incorporated into many recent geotechnical design codes, introduces the application of partial or resistance factors to selected characteristic values. Partial or resistance factors are usually set by national standard organizations, while characteristic values of geotechnical parameters are selected by engineers, often based on sparse measurement data combined with subjective engineering experience and judgment. Due to this subjective selection and individual judgment, the characteristic value derived by different engineers from the same dataset may vary greatly, especially when the test data contain significant variability. To address this issue, a new method based on Bayesian compressive sampling (BCS) is proposed in this study. BCS is able to reconstruct a high-resolution geotechnical property profile from sparse measurement data and quantify the uncertainty, e.g., confidence interval (CI) associated with the interpreted profile. The quantified uncertainty in the BCS has a clear statistical meaning: the corresponding confidence level for a CI from the BCS is the expected coverage proportion (i.e., fraction) of the complete profile that falls within the CI, if all data points along depth can be measured to provide the complete profile. This statistical meaning can be used to facilitate objective determination of characteristic values for geotechnical properties.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".