Reliability of Land Use/Land Cover Assessment in Montane Nepal
Bibliographic record
Abstract
The Kangchenjunga Conservation Area (KCA, Nepal) was the subject of a comparative study on land use/land cover change, using the maps and air photographs available for 2 different years (1978/79 and 1992). Digitized land use maps for 1978 (LUM78) and topographical maps for 1992 (TOPO92) were first interpreted using a Geographic Information System (GIS); this was followed by comparative interpretation of black and white air photographs from the same years. Lelep, Sekhathum–Amjilesa, Syajunma and Ramsyampati were the 4 areas selected for analysis.The initial map interpretation of LUM78 and TOPO92 implied that considerable changes in land use/cover had occurred between 1978/79 and 1992. Forestland was shown to have decreased by 62.5% (23.15 km2), agricultural land to have increased by 35.7% (1.49 km2), and shrubland to have increased by 238.2% (30.16 km2). Grazing land, with an area of 22.57 km2 on the 1978/79 and 1992 imagery, appeared to have disappeared completely by 1992. An interpretation of air photographs for the same period, however, revealed that the actual changes were far smaller than those inferred from the map interpretation: decrease in forest and grazing lands by 14.9% (5.45 km2) and 77.9% (2.75 km2), respectively, and increase in agricultural and shrublands by 4.9% (0.21 km2) and 19.7% (4.41 km2), respectively. The results of a questionnaire survey of the local inhabitants confirmed that no significant changes had occurred. The discrepancies identified highlight the problems inherent in assigning land categories. In particular, distinctions made on the LUM78 material between shrub, grazing land, and barren land were inappropriate. Similarly, forest and shrublands were incorrectly assigned in TOPO92. Caution must be exercised when using such information; verification from other sources is needed.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".