Estimating thaw settlement of coarse-grained permafrost sediments
Bibliographic record
Abstract
Thaw settlement, a frequently reported issue for infrastructure built on permafrost, contributes to high maintenance costs, reduced life cycles, and compromised serviceability of infrastructure. This paper presents a new method to estimate the thaw settlement in coarse-grained permafrost sediments, crucial for northern infrastructure planning. Utilizing available test results from the Canadian Arctic, an overview of the existing data for coarse-grained permafrost sediments is presented. The proposed method uses input parameters derived from particle size distribution to estimate the minimum void ratio of thawed sediments. The minimum void ratio is then used to infer the thawed void ratio, enabling the calculation of thaw strain. The effectiveness of the approach is confirmed by validating predicted thaw strains against measured values for over 60 permafrost samples. A comparison with existing empirical methods shows improved accuracy and reduced bias, further supporting the applicability of the approach. Additionally, an average thawed void ratio assigned to seven groups of granular soils proved valuable for predicting thaw strain when only visual descriptions of sediments are available. Tailored for granular materials and utilizing easily obtainable index properties, this approach provides a reliable and cost-effective method for predicting thaw settlement, benefiting engineers and planners in infrastructure development.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".