Hazard Assessment and Typology of Cascade-Arranged Glacial Lakes in High Mountain Regions
Bibliographic record
Abstract
Glacial lake outburst floods (GLOFs) represent one of the most acute natural hazards in glacierized mountain environments, with increasing relevance as cryospheric systems respond to accelerated global warming. Traditionally, hazard assessments have focused on individual glacial lakes, evaluated through static morphometric indicators. However, such approaches often overlook the broader spatial and hydrological dynamics in which glacial lakes evolve4particularly in cascade systems where multiple lakes are aligned along the same drainage path. This dissertation addresses this analytical gap by exploring the physical, geomorphic, and systemic dimensions of glacial lake hazard, with a special focus on cascade-arranged lake systems. The research is structured as a cumulative dissertation and consists of five peer-reviewed studies conducted in four distinct high mountain regions: the Himalayas, Andes, Canadian Cordillera, and the St. Elias Mountains. Paper I presents a detailed hazard assessment of Imja Lake (Nepal), based on field-based moraine surveys, satellite remote sensing, and DEM differencing. It highlights the interaction of lake expansion, slope instability, and dead-ice subsidence, and shows that hazard in moraine-dammed lakes is inherently multi-causal and cannot be evaluated through a...
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".