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Record W4381621865 · doi:10.31315/psb.v4i1.8908

Analisis Kesesuaian Wisata Di Pantai Glagah, Kalurahan Glagah, Kapanewon Temon, Kabupaten Kulon Progo, D.I. Yogyakarta

2023· article· id· W4381621865 on OpenAlexaff
Arya Dipa Aristo Putra, Johan Danu Prasetya, Dian Hudawan Santoso

Bibliographic record

VenueProsiding Seminar Nasional Teknik Lingkungan Kebumian SATU BUMI · 2023
Typearticle
Languageid
FieldSocial Sciences
TopicCommunity-based Tourism Development and Sustainability
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPhysicsForestryHumanitiesGeographyArt

Abstract

fetched live from OpenAlex

Pantai Glagah merupakan salah satu wisata pantai yang terletak di Kulon Progo yang termasuk kedalam Kawasan Strategis Pariwisata Daerah (KSPD) dimana didalamnya terdapat daya tarik wisata alam pantai dengan didukung oleh wisata kuliner tepi laut dan surga makanan hasil laut (Seafood Paradise). Pantai Glagah memiliki potensi bencana berupa gelombang pasang dan angin kencang. Tujuan dari penelitian ini adalah untuk menganalisis tingkat kesesuaian wisata. Penelitian ini menggunakan metode kuantitatif dan kualitatif. Data diperoleh melalui metode purposive sampling, wawancara, matematis berupa skoring, dan analisis deskriptif. Parameter kesesuaian wisata terdiri dari kedalaman perairan, lebar pantai, tipe pantai, kemiringan pantai, kecepatan arus, material dasar perairan, kecerahan perairan, biota berbahaya, ketersediaan air tawar, dan tutupan lahan. Berdasarkan hasil perhitungan analisis kesesuaian wisata di Pantai Glagah didapatkan hasil indeks kesesuaian wisata ∑IKW sebesar 64,90% yaitu sesuai (S2). Kata Kunci: Kesesuaian Wisata, Pantai, Wisata

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.020
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.038
GPT teacher head0.316
Teacher spread0.278 · 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 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
Published2023
Admission routes1
Has abstractyes

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