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Record W2954663951 · doi:10.36456/waktu.v15i1.436

REMOVAL COD DAN TSS LIMBAH CAIR RUMAH POTONG AYAM MENGGUNAKAN SISTEM BIOFILTER ANAEROB

2017· article· id· W2954663951 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

VenueWaktu · 2017
Typearticle
Languageid
FieldEngineering
TopicIndustrial Automation and Control Systems
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPhysicsChemistry

Abstract

fetched live from OpenAlex

Tingginya kandungan zat organik pada limbah cair industri rumah potong ayam (RPA) menyebabkan limbah cair tersebut tidak boleh dibuang langsung ke lingkungan akuatik. Peningkatan kebutuhan protein dari sumber konsumsi daging ayam, menyebabkan peningkatan limbah cair industri RPA. Oleh karena itu diperlukan suatu alternatif penyelesaian untuk menurunkan kandungan beban pencemar pada limbah cair industri RPA agar kualitas effluent yang dihasilkan tidak mencemari lingkungan serta memenuhi baku mutu yang telah ditetapkan. Pada penelitian ini pengolahan limbah cair RPA dilakukan dengan menggunakan sistem biofilter anaerob media bioball, dengan variasi waktu tinggal dan konsentrasi influnt. Sampel pengukuran konsentrasi Chemical Oxygen Demand (COD) influent berturut turut sebesar 734 mg/L, 388 mg/L, dan 248 mg/L. Konsentrasi Total Suspended Solid (TSS) dalam air baku limbah RPA sebesar 88 mg/L, 70 mg/L, dan 54 mg/L. Setelah dilakukan pengolahan mengalami penurunan konsentrasi COD dan TSS terhadap semua variasi konsentrasi. Waktu tinggal yang paling efektif dalam menurunkan kadar COD dan TSS pada limbah cair RPA adalah 7 jam.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.711
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.024
GPT teacher head0.242
Teacher spread0.218 · 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