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Record W4281669207 · doi:10.5539/enrr.v12n1p62

The Changes of Farmland by Using Spatial Cluster on the Case of BeiNan Township in Taiwan

2022· article· en· W4281669207 on OpenAlexvenueno aff
Wen‐Ching Wang, Ya-Ting Chan

Bibliographic record

VenueEnvironment and Natural Resources Research · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsnot available
FundersMinistry of Science and Technology, Taiwan
KeywordsUrban sprawlDatabase transactionScope (computer science)GeographyCluster analysisRegional scienceDistribution (mathematics)Land useSpatial changeBusinessEconomic geographyAgricultural economicsPhysical geographyEconomicsCivil engineeringComputer science

Abstract

fetched live from OpenAlex

The main objectives of this study are, (1) to investigate the influences of farmland policies on land use patterns and, (2) to examine the changes and statuses of land transactions and farmhouse construction projects and determine the influence of changes in the restrictions of farmland use and farmhouse construction on the trends of farmland transactions and transfers and the locations of farmhouse constructions projects, thereby validating the development trends of farmland and farmhouses. Aspatial analysis was performed to examine the spatial clustering conditions and locations of farmland transactions and farmhouses in Taitung County. Regional analysis was performed by examining local indicators of spatial association (LISA). A year-over-year analysis was performed on the scope and degree of farmhouse clustering within the target area to determine farmland use patterns, distribution statuses, and the impact of annual location change on rural development in the target area. An analysis of the historical land transaction and building change data revealed that "laws and regulations" were the main factors influencing farmland transaction. The density of farmland transactions increased closer to main traffic routes. The findings of this study highlighted rural change and validated urban sprawl.

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.

How this classification was reachedexpand

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.646
Threshold uncertainty score0.569

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.270
Teacher spread0.236 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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