Case history examples of estimating OCR in structured and structured organic cohesive soil with field vane tests using the SHANSEP-based method
Bibliographic record
Abstract
Accurate estimation of the preconsolidation stress and overconsolidation ratio, OCR, in uniform cohesive soils using the field vane test is influenced by the combined effects of soil type and plasticity, geologic history, soil structure, and the presence of organic matter. Published empirical correlations for cohesive soils consider the effects of soil type and plasticity, but significant variability can occur with changes in soil structure and the presence of organic matter content. The adaption of the “stress history and normalized soil engineering properties” (SHANSEP) format improves the characterization of overconsolidated soils using field vane tests by applying a proposed empirical approach to identify structured soils from unstructured soils and updating the SHANSEP-based approach to separately evaluate stress history in structured and unstructured soils. Validation of the correlation coefficients for individual projects is needed as the approach is applied to new geologic materials and with potentially different field vane equipment and laboratory testing procedures used to characterize the soils. This additional testing provides an opportunity to improve the correlations for specific conditions and reduce the variability in current OCR assessments.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".