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Record W1726670001 · doi:10.29244/jitl.9.2.63-70

Agricultural Land Conversion and Land Use Change Dynamics in North Bandung Area

2007· article· en· W1726670001 on OpenAlex
Agus Ruswandi, Ernan Rustiadi, Kooswardhono Mudikdjo

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJurnal Ilmu Tanah dan Lingkungan · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Agroindustry Studies
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsAgricultureAgricultural landLand useGeographyAgricultural economicsForestryAgricultural scienceEnvironmental scienceEconomicsEcologyBiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score0.915

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.035
GPT teacher head0.212
Teacher spread0.177 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it