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Record W7089567632 · doi:10.23960/jrl.v2i2.26

PEMODELAN ELEVASI DAN KECEPATAN BANJIR PADA TIKUNGAN LUAR DEKAT OUTLET BANGUNAN GORONG-GORONG BBA.4B DAERAH IRIGASI BUMI AGUNG

2025· article· id· W7089567632 on OpenAlexaff

Bibliographic record

VenueJurnal Rekayasa Lampung · 2025
Typearticle
Languageid
FieldEnvironmental Science
TopicPolar Research and Ecology
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsHydrology (agriculture)Statistical analysisWatershedAir temperature

Abstract

fetched live from OpenAlex

Daerah irigasi Way Bumi Agung adalah salah satu daerah irigasi kewenangan pusat yang terletak di Kabupaten Lampung Utara Provinsi Lampung. Jaringan pada daerah irigasi ini putus pada tahun 2016 akibat terjadinya longsoran pada bangunan gorong – gorong Bba.4b yang berada dibawah saluran primer Sta 02+850. Salah satu penyebab terjadinya longsor diduga karena adanya banjir dan aliran air yang cepat pada tititk tinjauan yaitu posisi Tikungan luar Sungai Way Abung dekat dengan outlet dari bangunan Bba.4b. Tujuan dari penelitian ini adalah untuk mensimulasikan elevasi muka air dan kecepatan air pada saat banjir diposisi tinjauan serta mengetahui pengaruh kondisi banjir terhadap proses terjadinya longsor Bangunan Bba.4b. Penelitian ini memodelkan secara numerik skenario banjir Q25 pada Sungai Way Abung dari hilir Bendung Bumi Agung sampai dengan posisi titik tinjauan. Pemodelan dilakukan dengan menggunakan metode unsteady flow dengan bantuan perangkat lunak HEC- RAS 1D. Hasil pemodelan berupa elevasi muka air banjir kemudian diplot pada penampang melintang Bangunan Bba.4b sehingga diperoleh posisi elelvasi muka air banjir pada posisi dekat Outlet Bangunan Bba.4b. Hasil simulasi elevasi muka air serta kecepatan air pada kondisi banjir diposisi titik tinjauan adalah 54.56 m dan 0.76 m/s. Elevasi muka air banjir tersebut masih dibawah elevasi dasar outlet bangunan Bba.4b namun karena kecepatan air yang terjadi masih tergolong cepat, maka resiko pengikisan tanah yang dapat meyebabkan longsornya saluran irigasi tetap masih ada. Untuk memperkuat analisis maka perlu dilakukan kajian terkait jenis tanah yang ada serta tingkat kepekaannya terhadap erosi atau pengikisan.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.043
Threshold uncertainty score0.145

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0430.006

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 head0.011
GPT teacher head0.268
Teacher spread0.257 · 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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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