Bibliographic record
Abstract
Geographically, Nepal is divided into three regions, namely; the Terai, the hills, and the mountains. Nepal is prone to many types of disasters due to the various causes and one of the main causes is its geographic setting. Some disasters and hazards are prevalent to all over the country, some are area specific. Mountain and high altitude hazards are unique in nature and have distinct features and they pose several challenges for the rescue and relief operations. Disasters in mountain regions of Nepal have multi-dimensional effects on human life, property and the environment. The paper analyzes the mountain disasters, their nature and their impacts. It also focuses on the institutional as well as legal arrangements regarding disaster rescue. For this purpose, a qualitative descriptive and analytical method is applied to achieve the desired objectives of the study. This paper depends upon the secondary source of data available in several works of literature; journal articles, books, news articles, government reports, and websites. The paper finds that the frequencies of mountain disasters are low in comparison to other parts of Nepal, but they are diverse and complex. There are institutional and legal mechanisms for disaster risk reduction, but they are not adequate to respond mountain disasters effectively. All security agencies along with private sectors involving in mountain search and rescue operations do not have sufficient mountain-specific rescue units, training, and logistics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".