When do rock glacier fronts fail? Insights from two case studies in South Tyrol (Italian Alps)
Bibliographic record
Abstract
Abstract The fronts of two rock glaciers located in South Tyrol (Italian Alps) failed on 13 August 2014, initiating debris flows in their downslope channels. A multimethod approach including climate, meteorological, and ground temperature data analysis, aerial image correlation, as well as geotechnical testing and modeling, led to the reconstruction of the two events. An integrated investigation of static predisposing factors, slowly changing preparatory factors, and potential triggering events shed light on the most likely reasons for such failures. Our results suggest that the occurrence of front destabilization at the two rock glaciers can only partly be explained by the occurrence of heavy rainfall events. Indeed, antecedent hydrological and thermal ground conditions were characterized by a saturated active layer favored by a snow‐rich winter and extensive precipitation in late spring and summer. Also, the rising trend of air temperature during spring and summer months since 1950s might explain the concurrent marked displacement of the two rock glaciers. Indeed, geotechnical investigations have provided strong indications that one of the investigated rock glacier fronts was at a marginally stable state prior to 2014. As rainfall events more intense than the one that occurred in August 2014 were previously recorded in the same area without resulting failures at the studied rock glaciers, we propose that both predisposing and preparatory destabilizing factors have played a key role in the 2014 rock glacier front failures.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".