Identification of glacier motion and potentially dangerous glacial lakes in the Mt. Everest region/Nepal using spaceborne imagery
Bibliographic record
Abstract
Abstract. Failures of glacial lake dams can cause outburst floods and represents a serious hazard. The potential danger of outburst floods depends on various factors like the lake's area and volume, glacier change, morphometry of the glacier and its surrounding moraines and valley, and glacier velocity. Remote sensing offers an efficient tool for displacement calculations and risk assessment of the identification of potentially dangerous glacial lakes (PDGLs) and is especially helpful for remote mountainous areas. Not all important parameters can, however, be obtained using spaceborne imagery. Additional interpretation by an expert is required. ASTER data has a suitable accuracy to calculate surface velocity. Ikonos data offers more detail but requires more effort for rectification. All investigated debris-covered glacier tongues show areas with no or very slow movement rates. From 1962 to 2003 the number and area of glacial lakes increased, dominated by the occurrence and almost linear areal expansion of the moraine-dammed lakes, like the Imja Lake. Although the Imja Lake will probably still grow in the near future, the risk of an outburst flood (GLOF) is considered not higher than for other glacial lakes in the area. Potentially dangerous lakes and areas of lake development are identified. There is a high probability of further lake development at Khumbu Glacier, but a low one at Lhotse Glacier.
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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 | 0.000 |
| 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".