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Upaya Mitigasi Bencana pada Sungai Kedunglarangan di Kabupaten Pasuruan

2024· article· id· W4401217826 on OpenAlex
Fathurrozi Ibnu Wardana, Linda Prasetyorini, Runi Asmaranto

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 Teknologi dan Rekayasa Sumber Daya Air · 2024
Typearticle
Languageid
FieldEnvironmental Science
TopicWater and Land Management
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsPhysicsForestryGeography

Abstract

fetched live from OpenAlex

Kecamatan Bangil, Kabupaten Pasuruan yang dilalui oleh Sungai Kedunglarangan bagian hilir merupakan kawasan rawan bencana banjir setiap tahunnya pada musim penghujan. Studi ini membahas upaya mitigasi bencana banjir tersebut dengan rencana normalisasi sungai sebagai pengendalian banjir, serta membuat rute evakuasi untuk mengungsikan penduduk dari ancaman bahaya ke lokasi yang lebih aman selama bencana berlangsung. Dalam analisa hidrologi yang dilakukan untuk mengetahui luas genangan pada Sungai Kedunglarangan, aliran banjir rancangan yang dihitung menggunakan Hidrograf Satuan Sintetik (HSS) Nakayasu sebesar Q25 = 781,567 m3/det serta Q50 = 858,632 m3/det. Pemodelan hidraulik menggunakan program HEC-RAS 2D versi 6.3.1 dengan perolehan luasan yang disebabkan banjir rancangan tersebut sebesar 3615,732 ha dengan kala ulang 25 tahun dan 3701,611 ha untuk kala ulang 50 tahun. Dilakukan juga pemodelan pengendalian banjir berupa pembuatan tanggul serta normalisasi sungai dengan fitur channel modification di RAS-Mapper

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.217
Threshold uncertainty score0.999

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 studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.004

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 head0.013
GPT teacher head0.232
Teacher spread0.218 · 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