Geostatistical analysis of cone penetration test (CPT) sounding using the modified Bartlett test
Bibliographic record
Abstract
More in situ tests are typically carried out over the same volume of soil in comparison to laboratory tests on undisturbed borehole samples. Hence, geostatistical analysis of in situ test records should in principle provide a more accurate and representative overview of spatial variation. A natural probabilistic model for correlated spatial data is the random field. Although the random field provides a concise description of spatial variation, it poses considerable practical difficulties for statistical inference because of the underlying autocorrelation structure. This note presents an extended discussion of the modified Bartlett random field estimation procedure, which is capable of rejecting the null hypothesis of weak stationarity for spatially correlated data. In comparison with simple visual inspection and the standard run test, the modified Bartlett test is shown to provide three advantages: (i) it is a more consistent measure that is unaffected by the vagaries of subjective interpretation; (ii) it is sufficiently discriminative to decide if a section is stationary, even when visual clues are ambiguous; and (iii) it is capable of accommodating realistic constraints (e.g., short record length). The possibility of identifying secondary soil boundaries that may not be readily apparent from visual inspection of cone soundings, its robustness to alternate transformations of the cone data, and the sensitivity of the proposed procedure to different levels of significance are discussed.Key words: geostatistics, random field, stationarity, modified Bartlett test, level of significance, run test.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".