Grading model for fine soil classification using cone penetration testing
Bibliographic record
Abstract
The grading of fine-grained soils is one of fundamental properties, and studying how to obtain this information using in situ methods is essential. However, existing cone penetration tests (CPT) lack a direct approach for measuring particle size and its content. To address that issue, the relationship between cone tip resistance ( qc) and sleeve friction resistance ( fs) with these curve parameters (CPs) was established using in situ CPT. Finally, an expression for the grading curves based on the results obtained from the CPT was presented. The main conclusions are as follows: (1) An exponential relationship exists between fine content and the variation pattern of fitting parameters used to characterize the grading curve, depending on the soil layer type. (2) The depth-corrected CPs are related to the qc and fs by a power function, revealing a link between the CPT and grading curve; (3) by utilizing global CPT and soil layer data, the boundaries of CPs were precisely defined, and the content of different particle sizes across various soil layers was predicted. The applicability of the fitting results was then analyzed for both homogeneous fine-grained soils and complex soil type.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".