Perceptions of climate change by highland communities in the Nepal Himalaya
Bibliographic record
Abstract
The impacts of climate change in remote communities of the Himalaya have been relatively underexplored. This study combines traditional knowledge of people from three Village Development Committees (VDCs) of three districts of the high altitudinal regions in Nepal with scientific data to document the changes in climatic patterns, natural hazards, ecological systems and agricultural practices. The respondents perceived notable changes in the local climatic conditions, the frequency of natural disasters and ecological processes. Their perception of warming over the past 15–20 years parallels the increase in mean annual temperature recorded in the Thehe VDC of the Humla district, Tukuche VDC of the Mustang district and Lelep VDC of the Taplejung district from 1973 to 2012 by 0.02°C/year, 0.04°C/year and 0.01°C/year, respectively. Most respondents perceived an increase in the frequency of floods and landslides. The recorded average frequency of natural hazards including fire, flooding, landslide and avalanche has increased significantly from 1.5 ± 0.61 incidences/year for the period 1972–1991 to 10.4 ± 2.91 incidences/year for the period 1992–2011. Increased occurrence of pests and insects was also noted. The results show that climate change has already affected local communities and they are responding by spontaneously developing adaptive livelihood strategies.
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 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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".