A probabilistic methodology to estimate site-scale thaw settlement in permafrost terrain under climate change
Bibliographic record
Abstract
In permafrost terrain climate change poses a severe threat to infrastructure. Deterministic methods for predicting soil temperature profiles struggle to account for inherent uncertainties in soil properties and surface conditions such as spatial and temporal variations and heterogeneity in surface material characteristics. This paper addresses this limitation by developing a probabilistic thermal analysis model using Monte Carlo simulations in Python, integrated with TEMP/W software. The model provides an estimate of site-scale thaw depth and associated thaw settlement of permafrost sediments in Hudson Bay Railway region under the worst-case climate scenario predictions for 2023–2100. The results of this study indicate that understanding the initial ground temperature conditions is critical for realistic predictions of both short-term and long-term thaw depths and thaw settlement variability. This research reveals that climate warming trends will likely accelerate the rate and depth of permafrost thaw, as evidenced by the increasing variability of possible thaw depth and settlements, which become more diverse and exhibit multiple probabilities as climate warming intensifies throughout the century. The methodology was also used to understand the sensitivity of input parameters and identified moisture content and thawing and freezing indices as the key drivers influencing the magnitude and variability of estimated thaw settlement, respectively. The methodology presented in this study provides valuable information on the distribution of potential outcomes when climate change is incorporated into thaw prediction. This research builds on existing knowledge of uncertainties in permafrost modeling with climate change scenarios and contributes by providing a probabilistic framework that integrates these uncertainties into infrastructure resilience, serviceability, and maintenance assessments. • Initial temperature profile effects on the thawed layer and thaw settlement varies by time. • Thaw settlement's uncertainty in prediction is lower than thaw depth. • Probability distributions for thawed layer and thaw settlement evolve over time. • Moisture content is inversely related to thawed layer and thaw settlement. • Surface material is key in thawed layer and thaw settlement's uncertainty.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".