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Record W2281839559 · doi:10.1177/0734242x15589781

Composition and uses of anaerobic digestion derived biogas from wastewater treatment facilities in North America

2015· article· en· W2281839559 on OpenAlexaffabout
Jillian Lackey, Brant A. Peppley, Pascale Champagne, Andrew Maier

Bibliographic record

VenueWaste Management & Research The Journal for a Sustainable Circular Economy · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsQueen's University
Fundersnot available
KeywordsBiogasAnaerobic digestionWaste managementSewage treatmentWastewaterEnvironmental scienceComposition (language)BioenergyWaste treatmentBiogas productionPulp and paper industryBiofuelMethaneChemistryEngineering

Abstract

fetched live from OpenAlex

A study was conducted to determine the current knowledge of biogas production and its use at municipal wastewater treatment plants (WWTPs) across North America. Information was provided by municipal WWTPs across Canada and the US. It was determined that hydrogen sulfide (H2S) and silicon (Si) compounds had sufficient variability to be of concern. The only biogas production trend that could be identified was a possible seasonal relationship with sludge input and biogas production. Secondary analysis was performed to observe trends in biogas usage in urban areas larger than 150,000 in the US and 50,000 in Canada; 66% of facilities had anaerobic digestion systems and, of those, only 35% had an energy recovery system. Climatic, population, and socio-political influences on the trends were considered. The primary conclusion was that more data is required to perform significant analyses on biogas production and composition variation.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.696
Threshold uncertainty score0.398

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.043
GPT teacher head0.284
Teacher spread0.241 · 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 designSimulation or modeling
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

Citations17
Published2015
Admission routes2
Has abstractyes

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