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Record W3049126557 · doi:10.25105/psia.v1i2.6605

ANALISIS BENDUNGAN KERING (DRY DAM) CIAWI SEBAGAI UPAYA PENGENDALIAN BANJIR DKI JAKARTA

2019· article· id· W3049126557 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

VenueProsiding Seminar Intelektual Muda · 2019
Typearticle
Languageid
FieldComputer Science
TopicMultimedia Learning Systems
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsForestryGeography

Abstract

fetched live from OpenAlex

Pembangunan Bendungan Kering (dry dam) Ciawi bertujuan sebagai upaya pengendalian banjir DKI Jakarta yang diakibatkan luapan air Sungai Ciliwung. Bendungan kering adalah bangunan bendung yang dibangun untuk mengkontrol banjir dengan membiarkan aliran sungai mengalir dengan bebas selama kondisi normal. Tujuan penelitian ini adalah untuk mengetahui reduksi banjir Sungai Ciliwung Hulu akibat dibangunnya Bendungan Kering Ciawi, dengan melakukan analisis hidrograf satuan sintetik (HSS) untuk mendapatkan hidrograf banjir rancangan dengan periode ulang 2, 5, 10, 25, 50, dan 100 tahun, setelah itu dilakukan analisis penelusuran banjir lewat waduk untuk mendapatkan reduksi banjir Sungai Ciliwung. Hasil perhitungan didapatkan Bendungan Ciawi dapat mereduksi banjir Sungai Ciliwung Hulu sebesar Q2 = 1,24%, Q5 = 14,12%, Q10 = 20,08%, Q25 = 26,81%, Q50 = 31,27%, dan Q100 = 35,33%. Hasil ini membuktikan dengan dibangunnya Bendungan Kering Ciawi dapat mereduksi banjir Sungai Ciliwung Hulu, sehingga diharapkan dapat mereduksi banjir di wilayah DKI Jakarta.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.311
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0040.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.006

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 head0.020
GPT teacher head0.259
Teacher spread0.239 · 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