Short-term velocity variations at three rock glaciers and their relationship with meteorological conditions
Bibliographic record
Abstract
Abstract. In recent years, strong variations in the speed of rock glaciers have been detected, raising questions about their stability under changing climatic conditions. In this study, we present continuous time series of surface velocities over 3 years of six GPS stations located on three rock glaciers in Switzerland. Intra-annual velocity variations are analysed in relation to local meteorological factors, such as precipitation, snow(melt), and air and ground surface temperatures. The main focus of this study lies on the abrupt velocity peaks, which have been detected at two steep and fast-moving rock glacier tongues ( ≥ 5 m a−1), and relationships to external meteorological forcing are statistically tested.The continuous measurements with high temporal resolution allowed us to detect short-term velocity peaks, which occur outside cold winter conditions, at these two rock glacier tongues. Our measurements further revealed that all rock glaciers experience clear intra-annual variations in movement in which the timing and the amplitude is reasonably similar in individual years. The seasonal decrease in velocity was typically smooth, starting 1–3 months after the seasonal decrease in temperatures, and was stronger in years with colder temperatures in mid winter. Seasonal acceleration was mostly abrupt and rapid compared to the winter deceleration, always starting during the zero curtain period. We found a statistically significant relationship between the occurrence of short-term velocity peaks and water input from heavy precipitation or snowmelt, while no velocity peak could be attributed solely to high temperatures. The findings of this study further suggest that, in addition to the short-term velocity peaks, the seasonal acceleration is also influenced by water infiltration, causing thermal advection and an increase in pore water pressure. In contrast, the amount of deceleration in winter seems to be mainly controlled by winter temperatures.
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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".