Estimation of the segregation potential of fine-grained soils using the frost heave response of two reference soils
Bibliographic record
Abstract
The frost heave response of quarry fines from several locations in the Province of Quebec was studied in the laboratory using one-dimensional step-freezing tests with free access to water. Comparison of the segregation potential values obtained from these tests with available data on fine-grained soils revealed the importance of including clay mineralogy and overburden effects in any predictive empirical relationship, especially when fines are nonclays. A new approach is presented to estimate segregation potential values using the frost heave response of two reference soils. The reference characteristics consist of a relationship between segregation potential at zero overburden pressure, specific surface area, and average grain size of the fines fraction for two artificial soil mixtures in which the clay mineral is poorly crystallized kaolinite. The prediction of segregation potential values using the reference frost heave characteristics approach is more robust and reliable than other empirical approaches that do not specifically distinguish between clay and nonclay fines. Furthermore, the new approach was also efficient for the assessment of frost susceptibility of well-graded glacial tills.Key words: fine grained, soil, mineralogy, laboratory, fines, clay, nonclay.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".