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Record W2620998417 · doi:10.36456/waktu.v15i1.426

PENGARUH BEBAN HIDROLIK MEDIA DALAM MENURUNKAN SENYAWA AMMONIA PADA LIMBAH CAIR RUMAH POTONG AYAM (RPA)

2017· article· id· W2620998417 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
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPhysicsChemistryAmmoniaOrganic chemistry

Abstract

fetched live from OpenAlex

Limbah rumah potong ayam (RPA) umumnya mengandung zat pencemar seperti Biological Oxygen Deman (BOD), Chemical Oxygen Deman (COD) dan Amonia yang tinggi. Umumnya senyawa pencemar tersebut terbentuk dalam pencernaan lipid. Kandungan ammonia pada limbah rumah potong ayam umumnya melebihi baku mutu yang sudah ditetapkan. Biofilter anaerob merupakan salah satu metode pengolahan limbah cair yang dapat diterapkan untuk mengolah air limbah RPA. Tujuan yang ingin dicapai adalah mengkaji kemampuan beban hidrolik media dalam menurunkan senyawa amonia pada air limbah RPA. Beban hidrolik media yang digunakan terdiri dari tiga variasi yaitu diantaranya 0,006 m3/m2media.hari, 0,009 m3/m2media.hari dan 0,015 m3/m2media.hari. Media yang digunakan dalam penelitian ini yaitu media karbon aktif untuk menurunkan beban pencemar ammonia pada air limbah RPA dengan sistem biofilter anaerob tercelup aliran upflow. Reaktor yang digunakan dalam percobaan ini adalah terdiri dari 3 reaktor dengan ukuran berbeda-beda. Efisiensi penyisihan kandungan amonia disimpulkan bahwa penerapan beban hidrolik media 0,006 m3/m2media.hari mampu menyisihkan senyawa ammonia lebih dari 95%.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.239
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0030.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.011

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 head0.021
GPT teacher head0.258
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