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Record W4390780437 · doi:10.36448/jts.v14i2.3477

ANALISA LUAS GENANGAN MENGGUNAKAN APLIKASI HEC-RAS 6.1 SECARA 3D DENGAN LUAS GENANGAN BANJIR YANG PERNAH TERJADI PADA DAS WAY PISANG (STUDI KASUS: DAS WAY PISANG)

2023· article· id· W4390780437 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 Teknik Sipil/Jurnal Teknis Sipil · 2023
Typearticle
Languageid
FieldComputer Science
TopicMultimedia Learning Systems
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsPhysicsForestryGeography

Abstract

fetched live from OpenAlex

Daerah Aliran Sungai (DAS) Way Pisang merupakan salah satu DAS yang terdapat pada Kabupaten Lampung Selatan dan memiliki sungai yang bermuara di Sungai Way Sekampung. Das Way Pisang mengalami fenomena alih fungsi lahan (land use) yang terjadi hampir disepanjang aliran dari hulu hingga hilir, sehingga setiap tahun selalu terjadi bencana banjir di beberapa lokasi sepanjang aliran Sungai Way Pisang. Penelitian ini menggunakan beberapa aplikasi bantuan yaitu ArcGIS 10.7.1, Global Mapper v23.0 dan HECRAS 6.1. Metode penelitian yang digunakan adalah dengan simulasi luas banjir menggunakan HEC-RAS 6.1 secara 3D. Dengan tipe aliran unsteady flow, dan menampilkan luas genangan banjir menggunakan RAS Mapper. Penelitian yang dilakukan ini mendapatkan hasil perubahan land use secara 3D sehingga dapat terlihat secara nyata ketinggian yang akan terjadi pada daerah yang mengalami banjir tersebut.

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)0.011
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.458
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.003
Meta-epidemiology (narrow)0.0050.005
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0040.009
Science and technology studies0.0060.002
Scholarly communication0.0060.004
Open science0.0110.005
Research integrity0.0020.008
Insufficient payload (model declined to judge)0.0010.010

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 head0.031
GPT teacher head0.297
Teacher spread0.266 · 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