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Record W4402279546 · doi:10.36985/704cmt03

Analisis Spasial Kesesuaian Fungsi Kawasan Daerah Aliran Sungai Bangop Dengan Rencana Tata Ruang Wilayah Kabupaten Tapanuli Tengah (Studi Kasus: Kecamatan Sarudik)

2024· article· id· W4402279546 on OpenAlexaff
Ahyar Abdi Nasution, Jef Rudiantho Saragih, Ummu Harmain

Bibliographic record

VenueJurnal Regional Planning · 2024
Typearticle
Languageid
FieldArts and Humanities
TopicArchitectural and Urban Studies
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsGeography

Abstract

fetched live from OpenAlex

Tujuan penelitian untuk mengetahui tingkat kesesuaian fungsi kawasan eksisting Daerah Aliran Sungai Bangop di Kecamatan Sarudik dengan rencana pola ruang RTRW Kabupaten Tapanuli Tengah (2013-2033) serta mengetahui lokasi fungsi kawasan terbaik dalam kaitannya menghindari daerah rawan banjir di Kecamatan Sarudik. Penelitian dilakukan menggunakan metode analisis spasial berbasis sistem informasi geospasial. Teknik yang digunakan adalah teknik overlay dan scoring terhadap peta - peta yang dibutuhkan untuk mendapatkan informasi lokasi dan keterangan sesuai kebutuhan yang telah direncanakan sebelumnya. Fotometri dilakukan menggunakan drone yang hasil fotonya diolah untuk dapat dipergunakan sebagai peta dasar digitasi penggunaan lahan aktual di Kecamatan Sarudik. Penelitian mengenai sebaran dan kondisi daerah resapan Kecamatan Sarudik mendapatkan hasil bahwa terdapat kondisi daerah resapan tidak kritis 4.306,61 Ha (95,29%), dan kondisi daerah resapan kritis 212,75 Ha (4,71%). Tingkat kesesuaian fungsi kawasan di Kecamatan Sarudik dengan rencana pola ruang RTRW Kabupaten Tapanuli Tengah (2013-2033) adalah Hutan Lindung (99,90%), Hutan Produksi Konversi (91.34%), Kawasan Perikanan (99,99%), Pemukiman (99,99%), dan Sempadan Sungai (70,04%). Sebagai upaya menghindari dampak banjir, diperoleh lahan seluas 242,07 Ha yang berada diluar daerah kerawanan banjir dan memiliki kondisi daerah resapan yang baik

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.075
GPT teacher head0.289
Teacher spread0.215 · 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
Published2024
Admission routes1
Has abstractyes

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