Global, consistent, and efficient production of transient permafrost ensemble simulations for investigating climatic influences on slope failures
Bibliographic record
Abstract
The magnitude and frequency of slope failures in permafrost zones have increased in recent decades. Permafrost warming and thawing represent major contributing factors to large slope failures, which have the potential to damage infrastructure and pose a risk to human life and surrounding ecosystems. Understanding the link between permafrost thaw and slope movement is thus crucial for identifying and adapting to related geohazards and increasing public safety in mountain communities.We aim to provide quantitative and time-dependent context for interpreting past events and establish correlations between slope failures and potential driving factors, such as changes in air temperatures, ground temperatures, thaw depth, and water availability. We demonstrate our system by investigating the change in these driving factors and their connection with recent slope movements in northern British Columbia and the Yukon.We developed a simulation workflow to generate 1D ensemble simulations of the ground thermal regime at any point globally, whose parameterization is helped by in-situ observations where available. Furthermore, we model temperature inversions in sub-arctic valleys where cold-air pooling is particularly intense in cold months and use it to correct 75 years of atmospheric reanalysis data forcing, increasing the accuracy and reliability of our results. We then produce summary statistics of drivers at permafrost landslide sites. This full-scale analysis is carried out for sites with varying degrees of remoteness, topographic parameters, and atmospheric conditions, producing an ‘ensemble’ of simulations. This framework allows for consistent and efficient production and analysis of mountain permafrost simulations in relation to slope failures. However, its main strength and appeal lie in its ability to be used globally and for a large number of sites, efficiently. Most workflows are contained in the Python packages SuPerSim and GlobSim, new packages for model testing and for producing climate change scenarios are added.We observe a general increase in extreme events in the variables we analyze compared to earlier decades, and we correlate their timings with those of landslides. Such research may help establish proxies for permafrost landslide preconditioning and triggers, providing a tool to support research and prediction concerning hazards in mountain terrain.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".