An improved approach for thaw depth evaluation considering unfrozen water in frozen soil
Bibliographic record
Abstract
The thaw depth of permafrost is known to depend on the ice content in frozen soil.Current engineering practice of thaw depth calculation often ignores the unfrozen water in permafrost and assumes that all water in the soil completely freezes below the water freezing temperature (e.g., 0 °C).Fine-grained soil typically contains silts and clays and may have significant amounts of unfrozen water below freezing temperature.The assumption of ignoring the unfrozen water leads to a less conservative estimation of the thaw depth by typically about 10% to 20% (up to 30%) for fine-grained soils.There is a need to develop a practical and industry-friendly approach for thaw depth assessment, which takes account of the unfrozen water content in frozen soil.The studies by Tice et al. (1976), Anderson and Ladanyi (2003) and Qu and Pham (2023) suggested a correlation between unfrozen water content in frozen soil and temperature below freezing point.The correlation (Qu and Pham 2023) can be established using the liquid limit and total water content of soil, which are available for most commercial projects.This paper presents an improved approach for thaw depth evaluation to take account of the unfrozen water content in frozen soil using the correlation associated with temperature.1
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".