Thaw Consolidation Properties of Fine-Grained Permafrost Soils of the Mackenzie Valley, Canada
Bibliographic record
Abstract
In this study, the thaw consolidation properties of fine-grained permafrost soils are investigated in terms of the characteristics of the relationships between the effective stress (σv′), the void ratio (e), and the hydraulic conductivity (kv). A total of 182 individual soil samples are included in the analysis covering a wide range of hydromechanical properties. The data are obtained from geotechnical studies undertaken for the Canadian Arctic Gas Pipeline project in the 1970s in the Mackenzie River Valley, Canada. The investigated characteristics are defined in agreement with the definition formulated by previous thaw consolidation models for fine-grained soils. Based on the general interpretation of the behavior of thawing soils, relationships are developed for the compression index of the thawed soil, the residual stress, the hydraulic conductivity change index of the thawed soil, and the initial hydraulic conductivity of the thawed soil. All properties are influenced by the ice content, which is characterized by the initial thawed void ratio for ice-rich soils and by the thawed void ratio for ice-poor soils. The liquid limit, the clay content, and the median grain size of the fine fraction are used as predictive parameters in combination with the initial ice content. The median grain size of the fine fraction yields the lowest error for the prediction of the characteristics of the σv′−e−kv relationships.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".