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Record W4409585429 · doi:10.36985/8gy3z150

Analisis Kebutuhan Sarana Dan Prasarana Pemadam Kebakaran Di Kabupaten Samosir

2024· article· id· W4409585429 on OpenAlexaff
Ferdy Berger Simbolon, Ummu Harmain

Bibliographic record

VenueJurnal Regional Planning · 2024
Typearticle
Languageid
FieldSocial Sciences
TopicLegal Studies and Policies
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPhysics

Abstract

fetched live from OpenAlex

Seiring dengan penetapan Danau Toba menjadi Kawasan Strategis Pariwisata Nasional (KSPN) melalui Peraturan Presiden Nomor 18 Tahun 2020, Kabupaten Samosir mulai berbenah diri dengan mulainya pembangunan-pembangunan yang masif. Pesatnya perkembangan fisik kota memerlukan rencana dalam pengendalian dari berbagai aspek termasuk perencanaan infrastruktur kebakaran wilayah. Penanganan kebakaran di Kabupaten Samosir masih menghadapi berbagai kendala, diantaranya. terlambatnya informasi yang diterima pihak terkait pada saat kejadian kebakaran, jarak tempuh ke lokasi yang jauh, padatnya permukiman serta sistem operasional dan kelengkapan sarana prasarana pemadam kebakaran yang terbatas. Penelitian ini bertujuan untuk mengetahui jumlah dan sebaran sarana prasarana pemadam kebakaran yang dibutuhkan di Kabupaten Samosir. Data dianalisisn menggunakan metode kuantitatif dengan pendekatan spasial. Penentuan sarana dan prasarana menggunakan analisis spasial, menggunakan alat analisis arcgis teknik overlay dan buffering untuk mengidentifikasi hubungan antara suatu titik dengan area sekitar. Hasil penelitian menunjukkan bahwa diperlukan penambahan sebanyak 11 pos sektor dan 5 pos pemadam kebakaran, dimana masing-masing pos sektor membutuhkan 2 unit kendaraan pemadam kebakaran dan setiap pos membutuhkan 1 unit kendaraan pemadam kebakaran kapasitas 4000 liter, dan setiap pos membutuhkan personil sebanyak 2 regu (12 orang) serta hydran sebanyak 181 unit

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.321
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
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.064
GPT teacher head0.345
Teacher spread0.280 · 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 teacher head, not a consensus.

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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