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Record W4211124567 · doi:10.31227/osf.io/sa9hv

IDENTIFIKASI LOKASI RAWAN BENCANA BANJIR LAHAR DI DAERAH ALIRAN SUNGAI PABELAN, MAGELANG, JAWA TENGAH

2017· preprint· id· W4211124567 on OpenAlexaff
Ahmad Cahyadi, Suprapto Dibyosaputro, Henky Nugraha, Danang Sri Hadmoko

Bibliographic record

Venuenot available
Typepreprint
Languageid
FieldEnvironmental Science
TopicWater and Land Management
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsLaharForestryGeographyGeologyPyroclastic rock

Abstract

fetched live from OpenAlex

Daerah Aliran Sungai (DAS) Pabelan merupakan salah satu sungai yang paling rawan mengalami banjir lahar pascaerupsi Gunungapi Merapi tahun 2010. Kejadian banjir lahar merusak di DAS ini terjadi sebanyak 17 kali sejak erupsi tahun 2010, terbanyak kedua setelah DAS Putih. Penelitian ini bertujuan mengidentifikasi wilayah rawan banjir lahar berdasarkan pada sensus dampak banjir lahar yang terjadi pasca erupsi Merapi Tahun 2010. Sensus dilakukan dengan melakukan identifikasi lokasi yang mengalami kerusakan dengan citra penginderaan jauh resolusi tinggi di lokasi kajian. Selain itu, identifikasi dilakukan dengan wawancara dengan seluruh pemerintah tingkat dusun dan desa yang wilayahnya dilalui aliran Sungai utama Pabelan. Hasil analisis menunjukkan bahwa lokasi rawan bencana banjir lahar terdiri dari 27 titik yang tersebarmulai dari hulu sampai dengan hilir DAS Pabelan.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.003

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 head0.250
Teacher spread0.231 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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