Agricultural Land Conversion and Land Use Change Dynamics in North Bandung Area
Bibliographic record
Abstract
Agricultural land conversion is considered as one of an important issus in the developing areas. In spite of theimportance of informations on the quantity and the rate of land conversion as the basis of formulating the agricultural landconversion policy, those informations are limited. This research was conducted at Lembang and Parongpong Sub-District,Bandung District. The objectives of research are to identify the rate of agricultural land conversion and to measure thedynamic change of land use. Land use in 1992 and 2002 was evaluated by interpretating the result of 1992 and 2002 landsatimage using Geographic Information System (GIS) program. Shift Share analysis was conducted to know the dynamic changeof land use. Results of the study indicated that land conversion at Lembang and Parongpong Sub-District during the period of1992-2002 (ten years) about 3,134.49 ha (25%) or 313.5 ha (2,96%) per year. Forestland reduced the most, from 5,470 ha in 1992 to 1,746 ha in 2002 or reduced about 3,732.12 ha (68%) in ten years. While area of the bush was increased about2,780.20 ha (1,326%) during the same period, from 210 ha in 1992 to 2,990 ha in 2002. Low land was decreasedfrom 252 hain 1992 to 95 ha in 2002, up land was decreased from 3,856 ha in 1992 to 2,736 ha in 2002, mix farming was increasedfrom2,491 ha in 1992 to 4,358 ha in 2002, resettlement was increased from 359 ha in 1992 to 1,612 ha in 2002, bare wasdecreasedfrom 1,115 ha in 1992 to 217 ha in 2002, lake was decreasedfrom 52 ha in 1992 to 50 ha in 2002.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".