Integrated framework for characterization of spatial variability of geological profiles
Bibliographic record
Abstract
Despite recent efforts to characterize the uncertainties involved with geological profiles and soil and rock properties, there has been limited study on their spatial correlations and how such features may be included in the engineering decision-making process. This paper presents an integrated framework for geostatisical analyses, which incorporates the restricted maximum likelihood (REML) method with the Matérn autocovariance model. Statistical tests are conducted including those for data normality, constant variance, and outliers, which ensure the fundamental assumptions of REML are not violated in the residual analyses of site data, meanwhile offering simple checks for potential errors in the dataset. The proposed approach also allows quantification of uncertainties in the subsurface profiles at the unsampled locations. The approach is illustrated through investigations on spatial correlation features of geological profiles at two project sites in Hong Kong. The number of irregularly spaced boreholes varies from 150 to 350 in the two cases, and the large volume of data enables the variations in rockhead levels to be studied through the proposed framework. In addition, the existence of geological faults in one of the sites is found to significantly affect the spatial variability of the rockhead level, as indicated by the reduced scales of fluctuation and spatial dependence, which corresponds to increased uncertainty in areas intersected by faults.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".