Rock Glacier Velocity: An Essential Climate Variable Quantity for Permafrost
Bibliographic record
Abstract
Abstract Rock glaciers are distinctive debris landforms found worldwide in cold mountainous regions. They express the long‐term movement of perennially frozen ground. Rock Glacier Velocity (RGV), defined as the time series of the annualized surface velocity of a rock glacier unit or a part of it, has been accepted as an Essential Climate Variable Permafrost Quantity in 2022. This review aims to highlight the relationship between rock glacier velocity and climatic factors, emphasizing the scientific relevance of interannual rock glacier velocity in generating RGV products within the context of observed rock glacier kinematics. Under global warming, rock glacier velocity exhibits widespread (multi‐)decennial acceleration. This acceleration varies regionally in onset timing (from the 1950s to the 2010s) and magnitude (up to a factor of 10), and has been observed in regions such as the European Alps, High Mountain Asia, and the Andes. Despite different local conditions, a synchronous interannual velocity pattern prevails in the European Alps since the 2000s, highlighting the primary influence of climate. A common pattern is the seasonal velocity rhythm, which peaks in late summer to autumn and declines in spring. RGV assesses permafrost evolution via (multi‐)decennial and interannual changes in rock glacier velocity, influenced by air temperature shifts with varying time lags and snow cover effects. Although not integrated into the RGV products, seasonal variations should be examined. This rhythmic behavior is attributed to alterations in pore water pressure influenced by air temperature, snow cover, and ground water conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".