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Record W4244996924 · doi:10.31219/osf.io/6mc3s

Arahan Pengembangan Destinasi Wisata Kawasan Kaldera Batur Berbasis Sistem Informasi Geografis (SIG)

2021· preprint· id· W4244996924 on OpenAlexaff
Nyoman Arto Suprapto

Bibliographic record

Venuenot available
Typepreprint
Languageid
FieldSocial Sciences
TopicCommunity-based Tourism Development and Sustainability
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPhysicsForestryHumanitiesGeographyArt

Abstract

fetched live from OpenAlex

Kawasan Kaldera Batur memiliki beragam potensi pengembangan wisata namun juga sangat dibatasi pengembangannya karena kawasan ini ditetapkan sebagai kawasan konservasi di dalam rencana tata ruang Kabupaten Bangli dan Provinsi Bali. Pengembangan Kawasan dilakukan untuk mengkomodasi kepentingan ekonomi masyarakat namun sekaligus juga menjaga fungsi lindung kawasan tersebut. Dualisme fungsi kawasan tersebut mengharuskan adanya sebuah pendekatan yang yang tepat dalam mengembangkan destinasi wisata Kaldera Batur agar tercapai pembangunan pariwisata yang berkelanjutan. Salah satunya adalah pengembangan destinasi wisata berbasis Sistem Informasi Geografis (SIG). Arahan pengembangan destinasi wisata khususnya penyediaan fasilitas pariwisata pada Kawasan Kaldera Batur dilakukan dengan pendekatan kemampuan lahan yang dianalisis dengan Teknologi SIG. Berdasarkan hasil analisis ditemukan bahwa kelas kemampuan lahan pada Kawasan Kaldera Batur yang cocok untuk pengembangan fasilitas penunjang pariwisata adalah Kelas D dan E yaitu kelas lahan dengan kemampuan pengembangan agak tinggi dan tinggi seluas 14.096 Ha yang tersebar di Desa Batur Tengah, Batur Selatan, Songan A, Sukawana dan Abangbatudinding.

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)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.722
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0040.001
Scholarly communication0.0030.001
Open science0.0040.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.025
GPT teacher head0.282
Teacher spread0.257 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2021
Admission routes1
Has abstractyes

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