MétaCan
Menu
Back to cohort
Record W2889855185 · doi:10.31227/osf.io/4r5pk

POTENSI SUMBERDAYA AIR SUB DAS SERAYU

2018· preprint· id· W2889855185 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

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.

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)0.000
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 score0.367
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.004

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

Quick stats

Citations1
Published2018
Admission routes1
Has abstractyes

Explore more

Same topicWetland Management and ConservationFrench-language works237,207