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Record W2897678968 · doi:10.1680/jwarm.17.00014

Quantification of anaerobic digestion feedstocks for a regional bioeconomy

2018· article· en· W2897678968 on OpenAlexfundno aff
Robin Curry, María Natividad Pérez-Camacho, Robert W. Brennan, Stephen Gilkinson, Thomas Cromie, Percy Foster, Beatrice Smyth, Angela Orozco, Elaine Groom, Simon Murray, Julie-Ann Hanna, Mark Kelly, Morgan Burke, Aaron Black, Christine Irvine, David W. Rooney, Steven F. Glover, G. McCullough, Aoife Foley, Geraint Ellis

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Waste and Resource Management · 2018
Typearticle
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsnot available
FundersQueen's UniversityQueen's University BelfastEuropean Regional Development FundInvest Northern Ireland
KeywordsBiogasAnaerobic digestionBiorefineryWaste managementEnvironmental scienceBiodegradable wasteSewage sludgeGreenhouse gasManureBiogas productionBioenergyBiofuelSewageEngineeringMethaneChemistryAgronomy

Abstract

fetched live from OpenAlex

Anaerobic digestion (AD) for biogas production forms one of the fundamental building blocks of the bioeconomy, and a research programme has been under way in Northern Ireland, which culminated in the publication of a Biogas Research Action Plan 2020 in 2014. One important element of this programme was the identification of the need for an evidence base for the potential bioresource feedstocks. This paper reports the outputs of the quantification of feedstocks for AD research, which has identified the organic feedstocks available for biogas production on a regional basis and categorised these as: organic (biodegradable) fraction of municipal solid waste, sewage sludge, organic industrial and commercial wastes, manure from livestock, food wastes and energy crops. The research study further quantified the biogas and energy potential of these feedstocks and possible reductions in greenhouse gas emissions. The limitations of the research study are acknowledged and opportunities to address these and build on and extend the study are identified, including feedstocks for other bioeconomy processes and the application and further development of the biorefinery concept.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.845
Threshold uncertainty score0.363

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 communication0.0000.000
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 head0.011
GPT teacher head0.198
Teacher spread0.187 · 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 designTheoretical or conceptual
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

Citations14
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Civil Engineers - Waste and Resource ManagementSame topicAnaerobic Digestion and Biogas ProductionFrench-language works237,207