Resource Constraints and Management Options in Mountain Watersheds of the Himalayas; Proceedings of a Regional Workshop held 8-9 December, 2003, in Kathmandu, Nepal
Bibliographic record
Abstract
ICIMOD's People and Resource Dynamics in Mountain Watersheds of the Hindu Kush-Himalayas Project (PARDYP), funded by SDC, IDRC, and ICIMOD, has been carrying out research in middle mountain watersheds since 1996, focussing on natural resource degradation and community and farm-based options to promote rehabilitation of degraded lands and increase in on-farm productivity. This publication is a compilation of the papers presented at a wrap-up workshop for Phase 2 of the project in December 2003. It assembles the results of three years of research in the five bench mark watersheds by the country teams in China, India, Pakistan, and Nepal, together with some innovative work by others. The papers identify common watershed management issues, especially land use intensification and soil nutrient deficiencies; drinking and irrigation water shortages; and water quality problems. Soil erosion and forest degradation were found to be less significant than previously thought. Although agricultural productivity is still a significant issue, many opportunities were shown for increasing farm productivity using a proper mix of simple technologies and institutional linkages. These proceedings should serve as a valuable resource for researchers, development workers, policy makers, and students of natural resource management working in the Himalayan region.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".