GEOSPATIAL TECHNOLOGY APPLICATION IN LOCAL LEVEL LAND USE PLANNING IN NEPAL
Bibliographic record
Abstract
Abstract. Even though planning process particularly economic development plan and its implementation in Nepal has been initiated with first five-year plan in 1952/54, land resource planning was overshadowed and only regional level data base on land use, land system and land capability were produced by Land Resource Mapping Project in 1983/84 and made available for planners and decision makers for sectoral planning in regional scale. During past, different policies and national planning efforts were made for balanced use of country’s existing natural resources but Nepal has not practiced land-use planning for the country as a whole at local level. It is initiated only after ninth five year plan (1997–2002) with the establishment of National Land Use Project under Ministry of Land Reform and Management and formulation of National Land Use Policy 2013 and its revision in 2015 after devastating earthquake. Land use council, Land use technical committee, District level land use monitoring committee and VDC/municipality level land use committee are institutional set ups for implementing planning works done by National Land Use Project at district and local levels. Resource maps produced by different international agency associated with India, Canada, USA, Japan and Finland were worked as basis for formulation of local level land use plans. Presently National Land Use Project (NLUP) has prepared land resources maps, geo-database and reports covering almost half of total VDCs of the country moreover in Terai region. Seven components of land resources management required for local level land use planning are present land use map, soil map, land capability map, hazard risk map, land use zoning map, cadastral superimpose on land use zoning map along with its geo-database and report except VDC profile. In first time, geospatial technology-RS, GIS and GPS were extensively applied in preparation of all these resource maps and creation of their geo-database for local level land use planning.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".