Frozen-Soil Classification With Index Testing
Bibliographic record
Abstract
Classification of frozen soils was first developed by the U.S. Army Corps of Engineers' Cold Regions Research and Engineering Laboratory (CRREL) together with the Division of Building Research, National Research Council, Canada. This visual method of classification was adopted by ASTM in 1983 as designation D4083, currently ASTM D4083-07: Standard Practice for Description of Frozen Soils (Visual-Manual Procedure), Annual Book of ASTM Standards, ASTM International, West Conshohocken, PA. The current visual classification standard does not use any engineering index testing to classify the soils. This leaves the engineer with a qualitative assessment of soil–ice mass type, strength, and stability in which a more conservative and expensive design may be considered and even worse, an under design may occur. The engineer is in need of some index properties that can be readily measured to provide a more objective method to classify the soil–ice mass. This paper investigates the relationship between water content and density to frozen-soil classification. It was found that the water/ice content and dry density within the same frozen-soil class varies significantly, which may lead to different mechanical behavior or thaw settlement. Therefore, reporting the water (ice) content and frozen-soil density together with the frozen-soil classification helps the engineer to better evaluate thaw settlement and assess need for further testing.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.011 |
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".