Integrated Watershed Management for Land and Water Conservation and Sustainable Agricultural Production in Asia: Proceedings of the ADB-ICRISAT-IWMI Project Review and Planning Meeting 10–14 December 2001, Hanoi, Vietnam
Bibliographic record
Abstract
Erratic rainfall, land degradation, soil erosion, poverty and burgeoning population characterize the dry \nregions in Asia. To develop sustainable natural resource management options for increasing the agricultural \nproductivity and income of the rural poor in these regions, a new Integrated Farmer Participatory Watershed \nManagement Model was developed by ICRISAT in partnership with the national agricultural research \nsystems (NARS). This model was applied at selected benchmark locations in Asia by ICRISAT through \nthe project RETA 5812 “Improving management of natural resources for sustainable rainfed agriculture in \nAsia”, funded by the Asian Development Bank (ADB). The challenge for catchment research is to \ngenerate technologies and management systems that is now being addressed by the Management of \nSoil Erosion Consortium (MSEC) project RETA 5803 “Catchment approach to managing soil erosion in \nAsia”. This project is funded by ADB and the International Water Management Institute (IWMI) serves as \nthe facilitator. The workshop “Integrated Watershed Management for Land and Water Conservation and \nSustainable Agricultural Production in Asia” was held to review these projects. Forty-five scientists from \nChina, India, Indonesia, Laos, Nepal, Philippines, Thailand, and Vietnam participated in the workshop. \nThe objectives of the workshop were to: \n(1) Review the progress made at benchmark watersheds/catchments and synthesize the findings from \nthe work of the projects RETA 5812 and RETA 5803; (2) Discuss work plans for 2002, identify emerging \nissues and future strategies for sustainable use of natural resources for improving rural livelihoods \nthrough new initiatives; and (3) Discuss watershed development and management technologies. During \nthe period, a one-day workshop on Watershed Methodologies was also organized. The research papers \nbased on the work conducted for three years at different benchmark sites in Asia are covered in this \npublication. The multi-country and multi-institutional research findings about watershed/catchment \nmanagement reported here will serve as a valuable resource for the researchers, policymakers and \nstudents working in the area of sustainable management of natural resources
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".