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Record W2615171618 · doi:10.1680/jenes.16.00020

Bioremediation of pit toilet sewage

2017· article· en· W2615171618 on OpenAlexvenueno aff
Sudhakar M. Rao, Lydia Arkenadan, Nitish Venkateswarlu Mogili, Saksham K. Atishaya, Priscilla Anthony

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Reuse
Canadian institutionsnot available
Fundersnot available
KeywordsDenitrificationSewageEnvironmental chemistryNitrateChemistryNitrificationAerobic denitrificationAmmoniumToiletEnvironmental sciencePulp and paper industryDenitrifying bacteriaNitrogenEnvironmental engineering

Abstract

fetched live from OpenAlex

The paper examines the efficacy of anaerobic, aerobic and denitrification reactions in reducing organic carbon (C), ammonium and nitrate concentrations in pit toilet sewage. The anaerobic character of pit toilet sewage causes nitrogen (N) to prevail as ammonium rather than as nitrate ions. Anaerobic decomposition of organic carbon is initially resorted to reduce competition for ammonium oxidation during subsequent aerobic treatment of sewage. A mixture of air-dried cattle manure, sand and gravel is used as a biobarrier medium for nitrate reduction. Cattle manure serves as an affordable organic carbon source; sand particles act as a medium for attached bacterial growth, while gravel improves the permeability of the barrier. Batch tests showed that anaerobic reactions reduce chemical oxygen demand (COD) concentration in pit toilet sewage by 85%. Comparatively, aerobic reactions reduce ammonium concentration in sewage by 77% through assimilation, nitrification and adsorption; 6–10 h of contact between the biobarrier mix and nitrate leads to acceptable levels of denitrification (residual nitrate concentration < 45 mg/l). A modified twin-pit toilet that facilitates anaerobic decomposition of sewage in the first pit and aerobic treatment and denitrification of sewage in the second pit is constructed at Mulbagal town, Karnataka, India.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.749
Threshold uncertainty score0.207

Codex and Gemma teacher scores by category

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

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 head0.004
GPT teacher head0.180
Teacher spread0.176 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations12
Published2017
Admission routes1
Has abstractyes

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