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Record W2027238821 · doi:10.1021/es901561g

Production of Bio-Synthetic Natural Gas in Canada

2010· article· en· W2027238821 on OpenAlexafffundabout
Kevork Hacatoglu, P. James McLellan, David B. Layzell

Bibliographic record

VenueEnvironmental Science & Technology · 2010
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsQueen's UniversityUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNatural gasRenewable natural gasGreenhouse gasEnvironmental scienceSubstitute natural gasRenewable energyBiomass (ecology)CoalEnergy securityFossil fuelPower to gasEnvironmental engineeringNatural resource economicsWaste managementFuel gasEngineeringSyngasEconomicsEcologyChemistry

Abstract

fetched live from OpenAlex

Large-scale production of renewable synthetic natural gas from biomass (bioSNG) in Canada was assessed for its ability to mitigate energy security and climate change risks. The land area within 100 km of Canada's network of natural gas pipelines was estimated to be capable of producing 67-210 Mt of dry lignocellulosic biomass per year with minimal adverse impacts on food and fiber production. Biomass gasification and subsequent methanation and upgrading were estimated to yield 16,000-61,000 Mm(3) of pipeline-quality gas (equivalent to 16-63% of Canada's current gas use). Life-cycle greenhouse gas emissions of bioSNG-based electricity were calculated to be only 8.2-10% of the emissions from coal-fired power. Although predicted production costs ($17-21 GJ(-1)) were much higher than current energy prices, a value for low-carbon energy would narrow the price differential. A bioSNG sector could infuse Canada's rural economy with $41-130 billion of investments and create 410,000-1,300,000 jobs while developing a nation-wide low-carbon energy system.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.173

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.002
GPT teacher head0.154
Teacher spread0.152 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations26
Published2010
Admission routes3
Has abstractyes

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