An assessment procedure for glacial hazards in the Swiss Alps
Bibliographic record
Abstract
Glacial hazards such as ice avalanches, glacial lake outburst floods, and debris flows have caused severe damage in populated mountain regions such as the Swiss Alps. Assessment of such hazards must consider basic glaciological, geomorphological, and hydraulic principles together with experience gained from previous events. An approach is presented here to assess the maximum event magnitude and probability of occurrence of glacial hazards. Analysis of magnitude is based on empirical relationships derived from published case histories from the Swiss Alps and other mountain regions. Probability of occurrence is difficult to estimate because of rapid changes in the nature of glacial systems, the low frequency of events, and the high complexity of the involved processes. Here, the probability is specified in qualitative and systematic terms based on indicators such as dam type, geometry, and freeboard height (for glacial lakes) and tendency of avalanche repetition, precursor events, and increased water supply to the glacier bed (for ice avalanche events). The assessment procedures are applied to a recent lake outburst with subsequent debris flow and to an ice avalanche in the Swiss Alps. The results yield reasonable event maxima that were not exceeded by actual events. The methods provide first-order assessments and may be applied in dynamic mountain environments where population and infrastructure growth require continuous evaluation of hazards.Key words: glacial hazards, lake outburst, debris flow, ice avalanche, hazard assessment procedure, probability of occurrence.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".