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Analisis Hidrolika pada Uji Model Pelimpah Bendungan Warsamson Papua Barat dengan Pendekatan Perangkat Lunak Autodesk Computational Fluid Dynamics (CFD)

2024· article· id· W4390984348 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 Teknologi dan Rekayasa Sumber Daya Air · 2024
Typearticle
Languageid
FieldEnvironmental Science
TopicWater and Land Management
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsPhysicsHumanitiesArt

Abstract

fetched live from OpenAlex

Indonesia memiliki kekayaan sumber daya air yang melimpah dan dimanfaatkan untuk kesejahteraan rakyat. Beberapa permasalahan pun terjadi seperti banjir dan kekeringan yang melanda Kabupaten Sorong, Papua Barat. Upaya pemerintah dilakukan melalui Badan Wilayah Sungai (BWS) Papua Barat berupa pembanguanan Bendungan Warsamson. Tahap awal pembangunan diawali dengan perencanaan konstruksi oleh konsultan kemudian dilanjutkan pembuatan model fisik. Pemodelan fisik dilaksanakan di Laboratorium Pengelolaan Sumber Daya Air (PSDA) Terpadu Universitas Brawijaya. Selain model fisik dilakukan juga penyesuaian desain dalam bentuk model tiga dimensi dengan pendekatan perangkat lunak Autodesk Computational Fluid Dynamics (CFD) untuk mengetahui sifat hidrolis pada bendungan secara mendetil pada bangunan pelimpah Bendungan Warsamson. Penelitian ini menggunakan bantuan perangkat lunak lain berupa Autodesk AutoCAD dan Inventor. Luaran penelitian berupa hasil simulasi yang memuat data nilai profil muka air, kecepatan aliran, dan tekanan hidrostatis sebagai parameter utama dalam perencanaan Bendungan Warsamson. Hasil simulasi pengaliran debit banjir pada model tiga dimensi pelimpah Bendungan Warsamson memenuhi standar desain.

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 categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.375
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 studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.242
Teacher spread0.229 · 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