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Record W4410926569 · doi:10.63824/jptsp.v11i2.206

ANALISIS MITIGASI BENCANA ALAM DENGAN PENDEKATAN SISTEM INFORMASI GEOGRAFIS DI MAGELANG

2024· article· id· W4410926569 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJURNAL TEKNIK SIPIL PERTAHANAN · 2024
Typearticle
Languageid
FieldBusiness, Management and Accounting
TopicDecision Support System Applications
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsBusiness

Abstract

fetched live from OpenAlex

Secara geografis Magelang terletak antara 110001’51” dan 110026’58” Bujur Timur dan antara 7019’13” dan 7042’16” Lintang Selatan. Secara administratif, terbagi ke dalam 13 Kecamatan. Topografi Karisidenan Kedu secara umum merupakan dataran tinggi yang berbentuk basin (cekungan). Diapit oleh gunung Merbabu, Merapi, Andong, Telomoyo, Sumbing dan Pegunungan Menoreh,dengan dua sungai besar yang mengalir ditengahnya yaitu sungai Progodan dan sungai Elo. Tersusun dari formasi batuan Andesit tua dengan jenis tanah Aluvial, Regosol dan Latosol. Tingkat kemiringan lereng yang cukup curam dan dengan kondisi jenis tanah yang ada di Magelang dapat memicu kerentanan bencana alam. Tingkat curah hujan yang cukup tinggi dapat memicu bencana tanah longsor didaerah pegunungan dan lereng gunung, sedangkan di daerah yang lebih rendah terjadi bencana banjir. Oleh karena itu, berangkat dari factor internal dan eksternal tersebut maka diperlukan analisis mitigasi bencana geologi dengan pendekatan SIG dalam meminimalisir korban harta dan jiwa.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.585
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0060.004
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.022

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 head0.019
GPT teacher head0.257
Teacher spread0.238 · 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