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Record W2889855185 · doi:10.31227/osf.io/4r5pk

POTENSI SUMBERDAYA AIR SUB DAS SERAYU

2018· preprint· id· W2889855185 on OpenAlexaff
Indra Riyanto, M Widyastuti, Heru Hendrayana

Bibliographic record

Venuenot available
Typepreprint
Languageid
FieldEngineering
TopicWetland Management and Conservation
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPhysicsEnvironmental scienceForestryGeography

Abstract

fetched live from OpenAlex

Sub DAS (Daerah Aliran Sungai) Serayu terletak di Kabupaten Wonosobo JawaTengah dengan luasan 13682.19 ha. Sub DAS Serayu merupakan salah satu Sub DAS yangmemiliki peranan penting terhadap kondisi DAS Serayu, yaitu sebagai daerah imbuhan air.Pada saat ini, di bagian hulu Sub DAS Serayu telah dimanfaatkan secara intensif untukpertanian dan Wisata Kawasan Dieng sehingga memberikan pengaruh terhadap kuantitasdan kualitas air sungai, serta kondisi DAS. Tujuan penelitian ini adalah menganalisisbesarnya debit aliran Sub DAS Serayu, menganalisis kualitas air sungai Sub Das Serayu,dan menganalisis tingkat kekritisan Sub DAS Serayu. Besarnya debit aliran dihitungmenggunakan pendekatan neraca air metode Thornthwaite Mather dan divalidasi denganpengukuran lapangan. Kualitas air diukur langsung di lapangan dan di laboratorium.Pengukuran langsung meliputi suhu, daya hantar listrik (DHL) dan pH; sedangkanpengukuran di laboratorium meliputi Dissolved Oxygen (DO), Biochemical OxygenDemand (BOD), Chemical Oxygen Demand (COD), Total Suspended Solid (TSS), TotalDissolved Solid (TDS), nitrat, fosfat, sulfat, amonia, H2S, Fe, Mn, detergen, coli tinja, danminyak lemak. Kekritisan Sub DAS didekati dengan perbandingan besarnya debit alirandan kebutuhan air dalam Sub DAS. Hasil penelitian menunjukkan bahwa neraca air SubDAS Serayu probabilitas 60 % diperoleh Direct runoff (DRO) sebesar 274.659.736m3/tahun, sedangkan probabilitas 80 % diperoleh DRO sebesar 182.487.225 m3/tahun.Validasi hasil perhitungan debit neraca air diperoleh 15% lebih tinggi dari debitpengukuran. Parameter kualitas air yang melebihi ambang batas baku mutu kelas IImenurut Peraturan Pemerintah 82/2001 adalah coli tinja pada seluruh sampel; dan padabeberapa sampel untuk kadar Fe, detergen, minyak lemak, sulfida dan pospat hal tersebutdisebabkan oleh keterdapatan penggunaan lahan berupa pertanian intesif di wilayah huludiikuti kegiatan wisata, dominasi sawah di bagian tengah Sub DAS, serta dominanpermukiman di hilir Sub DAS. Hasil analisis kekritisan Sub DAS Serayu menunjukkanbahwa kondisi Sub DAS termasuk klasifikasi tidak kritis.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0300.007

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 head0.012
GPT teacher head0.222
Teacher spread0.209 · 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 designObservational
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".

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Citations1
Published2018
Admission routes1
Has abstractyes

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