An Overview of Glaciers Distribution in the Nepal Himalaya
Bibliographic record
Abstract
Correction: On 7th September 2015, Pei Wang was included as an author on this paper. He was omitted from the paper by mistake. John W. Pomeroy was removed as an author of the paper but was included in the Acknowledgements of the paper (p.26)Abstract:Glaciers in the Himalayas are the important resource for fresh water. Continuous releases of the water from these glaciers make an important contribution to the drinking water, agriculture, and hydropower supply of densely populated regions in south and central Asia. Glaciers are not only a necessity for the survival of the people living in the low lying areas but also for their prosperity. Therefore, special attention should be given to detail research in the distribution of the glaciers in the Himalayan region and its surroundings. Physical parameters of glaciers area, length, depth, elevation profiles were analyzed based on the data provided by WGMS and NSIDC (1989), which was updated in 2012. Machhapuchhre, Thyangbo, Cho Oyu, Taweche, Setta, Tingbo and Kanchanjanga glaciers were found as the smallest glaciers in terms of area (<1km2), mean length (< 2km) and mean depth (40m) in the Nepal Himalaya. Langtang Ngojumba, Barun and Yalung glaciers were found as the largest glaciers in terms of area (>50km2). Large difference between start and end elevation point of glaciers of Khumbu, Ngojumba, Imja, Langtang indicates coverage area profiles are large and located in steep slopes of the Nepal Himalaya, which may result in linear erosions and avalanches. This paper also discusses about the Glacier Lake Outburst Flood (GLOF) in the Himalayan region.HYDRO NEPAL Journal of Water, Energy and EnvironmentIssue No. 13, July 2013Page:20-27 Uploaded date: 3/12/2014
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".