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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 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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.997

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 studies0.0000.001
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 head0.002
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 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

Citations26
Published2010
Admission routes3
Has abstractyes

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