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Record W4392979163 · doi:10.24815/jimap.v7i2.21349

Pengembangan Kawasan Wisata Air Terjun Pria Laot, Sabang

2023· article· id· W4392979163 on OpenAlexaff
Cut Tasya Irayana, Zainuddin Zainuddin, Myna Agustina Yusuf

Bibliographic record

VenueJurnal Ilmiah Mahasiswa Arsitektur dan Perencanaan · 2023
Typearticle
Languageid
FieldSocial Sciences
TopicCommunity-based Tourism Development and Sustainability
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsBusiness administrationBusiness

Abstract

fetched live from OpenAlex

Abstrak Wisata pegunungan Air Terjun Pria Laot memiliki panorama alam yang asri dan memiliki udara yang bersih ditengah hutan. Saat ini sarana pariwisata pada kawasan wisata Air Terjun Pria Laot belum dapat digunakan sepenuhnya oleh pengunjung. Tujuan penelitian ini adalah untuk mengetahui kondisi eksisting dan memberikan arahan pengembangan sarana dan prasarana obyek wisata Air Terjun Pria Laot. Metode penelitian ini menggunakan analisis deskriptif dan analisis SWOT dari hasil pengumpulan data primer yang dilakukan dengan observasi, wawancara serta data sekunder yang dikumpulkan dari bahan bacaan. Hasil yang didapatkan adalah kondisi eksisting kawasan wisata Air Terjun Pria Laot memiliki atraksi wisata yang indah dan alami,namun kondisi jalan harus dilakukan perbaikan, ketersediaan transportasi umum sangat minim, tempat parkir dan kamar ganti/toilet belum sesuai dengan Peraturan Menteri, belum adanya sistem pengelolaan sampah dan tidak adanya paket wisata yang melayani perjalanan ke obyek wisata. Strategi prioritas berdasarkan SWOT adalah menambahkan pengembangan kawasan wisata Air Terjun Pria Laot dalam rencana Pemerintah Kota Sabang, meningkatkan kualitas, perbaikan dan pengadaan terhadap sarana serta melakukan promosi objek wisata air terjun.A

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.125

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.001
Scholarly communication0.0050.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0370.010

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 head0.029
GPT teacher head0.296
Teacher spread0.266 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2023
Admission routes1
Has abstractyes

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