Retrogressive thaw slump susceptibility in the northern hemisphere permafrost region
Bibliographic record
Abstract
Abstract Mean annual temperatures in the Arctic and subarctic have increased in recent decades, increasing the number of permafrost hazards. Retrogressive thaw slumps (RTSs), triggered by the thawing of ground ice in permafrost soil, have become more common in the Arctic. Many studies report an increase in RTS activity on a local or regional scale. In this study, the primary goals are to: (i) examine the spatial patterns of the RTS occurrences across the circumpolar permafrost region, (ii) assess the environmental factors associated with their occurrence and (iii) create the first susceptibility map for RTS occurrence across the Northern Hemisphere. Based on our results, we predicted high RTS susceptibility in the continuous permafrost regions above the 60th latitude, especially in northern Alaska, north‐western Canada, the Yamal Peninsula, eastern Russia and the Qinghai‐Tibetan Plateau. The model indicated that air temperature and soil properties are the most critical environmental factors for the occurrence of RTSs on a circumpolar scale. Especially, the climatic conditions of thaw season were highlighted. This study provided new insights into the circumpolar susceptibility of ice‐rich permafrost soils to rapid permafrost‐related hazards like RTSs and the associated impacts on landscape evolution, infrastructure, hydrology and carbon fluxes that contribute to global warming.
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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 | 0.000 |
| 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) | 0.001 | 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".