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Record W4381621866 · doi:10.31315/psb.v4i1.8910

Evaluasi Kesesuaian Lahan Kawasan Pariwisata di Pantai Kuwaru, Desa Poncosari, Kecamatan Srandakan, Kabupaten Bantul, Daerah Istimewa Yogyakarta

2023· article· id· W4381621866 on OpenAlexaff
Shella G Kakisina, Johan Danu Prasetya, Wisnu Aji Dwi Kristanto

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
KeywordsPhysicsForestryGeography

Abstract

fetched live from OpenAlex

Daerah Istimewa Yogyakarta memiliki potensi sumber daya pesisir yang berlimpah. Perkembangan wisata mulai meningkat sekitar tahun 2015-2019. Pantai Kuwaru adalah pantai yang strategis sebagai kawasan untuk wisata keluarga dan Pendidikan yang berada di bagian pantai selatan wilayah Bantul. Pada tanggal 5 Juni 2017 terjadi bencana abrasi yang mengakibatkan terjadinya kerusakan pada bangunan di Pantai Kuwaru. Tujuan dari penelitian ini yaitu mengevaluasi kesesuaian lahan untuk wisata Pantai Kuwaru. Metode yang digunakan dalam penelitian ini yaitu metode survei dan pemetaan, metode skoring dan pembobotan, dengan melakukan pengukuran pada 6 parameter yaitu tipe pantai, lebar pantai, material daar Perairan, kemiringan pantai, tutupan lahan pantai, dan ketersediaan air tawar. Berdasarkan evaluasi didapatkan hasil nilai kesesuaian lahan di Pantai Kuwaru sebesar 44 % yaitu kategori S3 (sesuai bersyarat). Penelitian ini kiranya dapat dijadikan evaluasi atau rekomendasi untuk mengelola dan mengembangkan Pantai Kuwaru kedepannya. Kata Kunci : Evaluasi; Kesesuaian Lahan; Pantai; Pariwisata

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)0.004
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0230.004

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 head0.046
GPT teacher head0.333
Teacher spread0.287 · 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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