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Analisis Hidraulika Uji Model Fisik Kantong Lumpur Bendung Gerak Karangnongko Kabupaten Bojonegoro Provinsi Jawa Timur

2023· article· id· W4398777186 on OpenAlexaff
Stefanus Wim Kristanto, Suwanto Marsudi, Dian Sisinggih

Bibliographic record

VenueJurnal Teknologi dan Rekayasa Sumber Daya Air · 2023
Typearticle
Languageid
FieldComputer Science
TopicMultimedia Learning Systems
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsGeographyForestry

Abstract

fetched live from OpenAlex

Pembangunan Bendung Gerak Karangnongko merupakan salah satu upaya dari Pemerintah Pusat melalui Balai Besar Wilayah Sungai (BBWS) Bengawan Solo dibawah Kementerian PUPR untuk meningkatkan efisiensi jaringan irigasi di Indonesia. Peningkatan efisiensi saluran irigasi dapat dilakukan dengan meminimalisir jumlah sedimen yang masuk ke dalam jaringan irigasi. Kantong Lumpur Bendung Gerak Karangnongko direncanakan untuk mengendapkan sedimen dan membuang sedimen kembali ke Sungai Bengawan Solo sehingga saluran irigasi pada DI (Daerah Irigasi) Karangnongko Kanan yang memiliki luas daerah pengaliran seluas 5.203 ha terbebas dari bahaya sedimentasi. Desain awal kantong lumpur telah dibuat oleh konsultan perencana, namun untuk menyempurnakan desain tersebut, maka diperlukan pemodelan fisik hidraulik agar kondisi hirdaulika aliran dapat diketahui dan nilai efektivitas flushing maksimal dapat dicapai. Pemodelan fisik hidraulik ini dilakukan di Laboratorium Pengelolaan Sumber Daya Air (PSDA) Terpadu Universitas Brawijaya. Setelah dilakukan berbagai upaya untuk menyempurnakan desain, kondisi hidraulika aliran pada model seri 1 (final design) sudah dalam kondisi yang sangat baik. Selain itu, efektivitas flushing reratanya sudah mencapai 91,58% dengan efektivitas flushing maksimal terjadi pada kompartemen 4 dengan persentase sedimen yang tergelontor mencapai 95,75%. The Karangnongko Barrage construction is one of the efforts made by the Ministry of Public Works through Balai Besar Wilayah Sungai (BBWS) Bengawan Solo to increase the efficiency of irrigation channel in Indonesia. To increace the efficiency of irrigation channel, it can be done by minimizing the amount of sediment that enters the irrigation channel. Karangnongko Barrage Settling Basin is planned to let the sediment settles and to flush sediment that enters the irrigation channel so that the irrigation channel on Karangnonko Kanan Irrigation Area is free from sediment. With an area of 5.203ha for the Karangnongko Kanan Irrigation Area, it is necessary to build a settling basin to minimize the amount of sediment entering the irrigation channel. A hydraulic model test is needed to improve the initial design made by the planning consultant so that the hydraulic conditions of the flow can be known and the maximum flushing effectiveness value can be achieved. The hydraulic model test is constructed in University of Brawijaya PSDA Terpadu Laboratory. After the design improvement is done, the hydraulic condition of the final design (series 1) flow is in good condition. The average flushing effectiveness has reached 91,58% with the maximum flushing effectiveness occuring at the compartment 4 with the percentage of sediment being flushed reaching 95,75%.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.271
Threshold uncertainty score0.540

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
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 head0.035
GPT teacher head0.277
Teacher spread0.242 · 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 designSimulation or modeling
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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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