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Record W2081708183 · doi:10.1504/ijgw.2009.027083

The reduction of greenhouse gas emissions using various thermal systems in a landfill site

2009· article· en· W2081708183 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational Journal of Global Warming · 2009
Typearticle
Languageen
FieldEnergy
TopicOil, Gas, and Environmental Issues
Canadian institutionsOntario Tech UniversityCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Ontario Institute of Technology
KeywordsGreenhouse gasLandfill gasWaste managementCogenerationEnvironmental scienceEnvironmental engineeringMunicipal solid wasteCombustionSolid oxide fuel cellElectricity generationEngineeringChemistry

Abstract

fetched live from OpenAlex

In this paper, the Greenhouse Gas (GHG) emissions from an uncontrolled landfill site filled with Municipal Solid Waste (MSW) are compared with those from controlled sites in which collected Landfill Gases (LFG) are utilised by various technologies. These technologies include flaring, conventional electricity generation technologies such as Internal Combustion Engine (ICE) and Gas Turbine (GT) and an emerging technology, Solid Oxide Fuel Cell (SOFC). The results show that SOFC is the best option for reducing the GHG emissions among the studied technologies. In the case when SOFC is used, GHG emissions from the controlled site are reduced by 63% compared to the uncontrolled site. This case has a specific lifetime GHG emission of 2.38 tonnes CO2 .eq/MWh when only electricity is produced and 1.12 tonnes CO2.eq/MWh for a cogeneration application.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.626
Threshold uncertainty score0.289

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 head0.013
GPT teacher head0.273
Teacher spread0.260 · 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