Volumetric quantifications and dynamics of areas undergoing retrogressive thaw slumping in the Northern Hemisphere
Bibliographic record
Abstract
Retrogressive thaw slumping (RTS) is a mass-wasting process characterized by upslope backwasting and rapid thawing of ice-rich permafrost. High-resolution digital elevation models (DEMs) from ArcticDEM enable the volumetric and soil organic carbon quantification of medium to large disturbance areas undergoing RTS ( ≥10,000 m2) for the Northern Hemisphere. Using DEM time-series analysis and deep learning, we retrieve a total of 2747 disturbance areas undergoing active RTS with a total volume loss of (317.0 ± 0.3) × 106 m3 between 2012 and 2022. Here we show that climatic drivers of RTS activity exhibit latitudinal and regional variations, specifically, the number of precipitation-driven RTS decreases linearly as latitudes increase, whereas temperature-driven RTS increases sharply. Finally, we estimate that 96% of detected RTS thawed ~1.95 × 10–3 Pg carbon per year, equivalent to ~0.2% of annual gradual thaw emission estimates. Our results highlight the complexity of regional RTS dynamics and the importance of high resolution, long-term monitoring efforts. This study systematically mapped 2747 retrogressive thaw slumps during the past decade. It finds that higher latitude thaw slumps are mainly driven by temperature, while lower latitudes are influenced by prior-year climate and precipitation.
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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 | 0.001 |
| 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 | 0.001 | 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".