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Record W2948523308 · doi:10.36441/seoi.v1i1.604

KETERCUKUPAN AIR TEMPORAL SEBAGAI INDIKATOR KETERSEDIAAN AIR KAWASAN (STUDI KASUS DAS CILIWUNG HULU)

2022· article· id· W2948523308 on OpenAlexaff
Agus Dwi Susanto, M. Yanuar J. Purwanto, Bambang Pramudya, Etty Riani

Bibliographic record

VenueSustainable Environmental and Optimizing Industry Journal · 2022
Typearticle
Languageid
FieldSocial Sciences
TopicCommunity-based Tourism Development and Sustainability
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsForestryEnvironmental scienceGeography

Abstract

fetched live from OpenAlex

DAS Ciliwung Hulu wilayahnya meliputi kawasan wisata puncak, menpunyai curah hujan rata-rata tahunan lebih besar dari 3.000 mm, namun beberapa kawasan mengalami kekurangan air baku diwaktu musim kemarau. Kondisi tersebut akibat pembangunan lahan untuk pariwisata atau pemukiman dengan laju 12.34% per tahun, sehingga air hujan yang masuk ke dalam tanah (infiltrasi) hanya 20%. Tujuan dari penelitian ini adalah untuk mengetahui ketersediaan air sepanjang tahun dari masing-masing sub DAS di DAS Ciliwung Hulu dengan indikator ketercukupan air temporal. Metode yang digunakan adalah: F.J. Mock untuk analisis debit andalan, Indeks Pollutan untuk analisis kualitas air, neraca air untuk surplus dan defisit air, dan indeks ketercukupan air temporal (IKaT). Hasil analisis menunjukkan: Sub DAS Ciseuseupan untuk katagori ketersediaan air termasuk ke dalam katagori tidak cukup, sedangkan dalam katagori ketercukupan air temporal masuk kurang cukup; Sub DAS Cibogo dalam katagori ketersediaan air masuk dalam status kurang cukup, namun dalam katagori ketercukupan air temporal termasuk ke dalam status sedang: sub DAS Cisarua baik untuk katagori ketersediaan maupun ketercukupan air temporal masuk dalam status sedang; dan sub DAS Ciesek, Ciliwung Hulu, dan Cisakabirus mempunyai skor 1 baik untuk ketercukupan air temporal maupun keterseiaan air.

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 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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.710
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0150.002
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0010.008
Insufficient payload (model declined to judge)0.0050.000

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 head0.246
Teacher spread0.236 · 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 teacher head, not a consensus.

Study designQualitative
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
Published2022
Admission routes1
Has abstractyes

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