Quantifying exposure of linear infrastructures to earthquake-triggered transverse landslides in permafrost thawing slopes
Bibliographic record
Abstract
Seismic shaking can cause slope instability in otherwise relatively stable permafrost terrains. In addition, rapid ice melting in low-permeability fine-grained soils can lead to excess pore-water pressure build-up and cause instability in slopes even at small angles. This study addresses the active-layer detachment (ALD) slope instability hazard and develops a systematic risk assessment framework for existing and future linear infrastructures, such as energy pipelines, bridges, and roads traversing permafrost regions. Mild slopes, with average gradient of 7°, are considered in this study as the most representative of actual field conditions. The potential for earthquake-triggered ALD is analytically quantified. State-of-the-art ALD morphological statistics for northern Canada are combined with seismic slope stability analyses to determine (i) the probability of linear infrastructure exposure to permanent ground deformations (PGDs) caused by ALD and (ii) the extent of the potential PGD that the linear infrastructure may be subjected to. The Monte Carlo technique is applied to simulate and assess the sensitivity of the model to parameters such as earthquake magnitude and source-to-site distance. Findings from this study can be used to evaluate the vulnerability of linear infrastructures exposed to the ALD hazard.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".