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Studi Perencanaan Kolam Retensi Untuk Menanggulangi Banjir pada Afvoer Watudakon Kabupaten Mojokerto

2024· article· id· W4390977599 on OpenAlex
Bima Wenas Arkananta, Dwi Priyantoro, Andre Primantyo Hendrawan

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 institutionsEncana (Canada)
Fundersnot available
KeywordsPhysicsForestryGeography

Abstract

fetched live from OpenAlex

Sungai Afvoer Watudakon berlokasi di Kabupaten Mojokerto sering mengalami banjir tahunan yang terjadi akibat alih fungsi lahan dari lahan pertanian menjadi pemukiman penduduk serta secara topografi posisi DAS Afvoer Watudakon berada di daerah cekungan, sehingga diperlukan upaya untuk mengurangi beban limpasan yang diterima oleh Sungai Afvoer Watudakon. Dalam studi ini digunakan rumus debit banjir rancangan metode drain module serta metode rasional dengan kala ulang Q25 = 132,94 m3/det. lalu dilakukan analisa hidraulik dengan aplikasi HEC-RAS 5.0.7 guna mendapatkan debit yang dapat dialirkan oleh siphon watudakon sebesar Q = 95,07 m3/det. dengan metode De Marchi didapatkan pelimpah samping sepanjang 25 m dan tinggi pelimpah 4,1 m dengan sudut masuk pelimpah 60°, serta luas kolam retensi dengan volume tampungan 205.314 m3 dengan 2 pompa berkapasitas 3 m3/det serta dilengkapi dengan dinding penahan tanah dengan tinggi 6 m yang telah disimulasikan terhadap beberapa kombinasi kondisi pada aplikasi Plaxis V20 dan dinyatakan aman terhadap semua kondisi

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
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 science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.005

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 head0.016
GPT teacher head0.235
Teacher spread0.220 · 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