Transformation models for effective friction angle and relative density calibrated based on generic database of coarse-grained soils
Bibliographic record
Abstract
This study compiles a generic database of seven parameters, including relative density and friction angle, for coarse-grained soils from 176 studies, covering a wide range of reconstituted and in situ coarse-grained soils. This database, labeled as “SAND/7/2794”, is dominated by data from laboratory reconstituted soils such as Erksak, Hokksund, Monterey, Ottawa, Sacramento River, Ticino, and Tonegawa sands. About 15% of the data points in the database are in situ samples obtained from tube sampling, block sampling, or ground freezing techniques. The correlation behavior among some parameters in the database is consistent with existing transformation models in the literature. Mine tailings, volcanic soils, railroad ballast, gravelly soils with significant cobble or boulder content, and soils with high fines contents are removed from the database because they exhibit inconsistent behavior. Soils subjected to very high effective stresses are also removed from the database. The generic database is adopted to calibrate the bias and variability of existing transformation models. Transformation uncertainties are characterized based on their bias, variability, and the range of applicability.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".