Short-term velocity variations of 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 in a changed climate. In this study, we present continuous time series over three years of surface velocities of six GPS stations located on three rock glaciers in Switzerland. Intra-annual velocity variations are analyzed in relation to local meteorological factors, such as precipitation, snow(melt), as well as air and ground surface temperatures. A main focus of this study lies on the abrupt velocity peaks, which have been detected at two steep and fast moving rock glacier tongues. The continuous measurements with high temporal resolution revealed that all rock glaciers experience clear intra-annual variations in movement where the timing and the amplitude is rather similar between individual years. The seasonal decrease in velocity was typically smooth, starting one to three months after the seasonal decrease in temperatures, and was stronger in years with colder temperatures in mid winter. The seasonal acceleration always started during the zero curtain period, often was abrupt and rapid compared to the winter deceleration, and at two stations it was interrupted by short velocity peaks, occurring immediately after high water input from snowmelt or heavy precipitation. The findings of this study suggest that both, the seasonal acceleration and the short velocity peaks are strongly influenced by water infiltration, causing thermal advection and increase in pore water pressure, and that likely no velocity peak was solely caused by high temperatures. 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 | 0.000 | 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".