Interface response-based soil classification framework
Bibliographic record
Abstract
Current soil classification systems based on cone penetration testing (CPT) utilize a combination of the tip resistance (qt), pore pressure (u2), and friction sleeve (fs) measurements as inputs. While the qt measurements are typically normalized by the overburden stress, the fs measurements are often normalized by the net tip resistance, leading to the use of parameters that are dependent on each other. This paper presents the development of a soil classification framework that utilizes a normalized multi-friction parameter (MFP) and the CPT normalized tip resistance. The MFP parameter is obtained from measurements with textured friction sleeves from soundings with multi-sleeve attachments. The use of textured friction sleeves allows for fundamental differences in soil–structure interface behavior and particle sizes to be captured due to the significant degree of shearing induced within the soil. This classification framework was developed with results from over 30 soundings at six different sites. The analysis of samples taken from the field indicates that the proposed framework provides a classification that better agrees with the grain-size distribution for residuum, calcareous and intermediate soils, as compared to existing CPT-based systems. The potential development of a simplified probe with just one additional friction sleeve sensor can provide appropriate classification results and would facilitate adoption for use in practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".