Land Use and Population Dynamics in the Kalikhola Watershed of Nepal
Bibliographic record
Abstract
This study analyzes the nexus between population dynamics and land-use practices in the Mid-Hill region of Nepal. The paper focuses on spatial and temporal changes in land use between 1987 and 1999 in a typical watershed in the western mountains of Nepal where community forest projects were implemented by the government. The dynamics of population, land use, and land cover within the Kalikhola watershed are investigated by performing spatial analysis of digital land-use maps in ArcGIS. There is a net increase in forest cover of 16% in the Kalikhola watershed with a corresponding decrease in agricultural land, shrubland, and grassland. The population of highland communities has been significantly reduced because of problems due to the implementation of community forest projects. In this watershed, a significant area under agriculture in 1987 was found abandoned in 1999, most likely because of increased out-migration of the labour force and frequent attacks of wild animals.
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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".