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Record W2133866862 · doi:10.2118/136866-pa

Natural Gas: The Green Fuel of the Future

2012· article· en· W2133866862 on OpenAlexaff
Robert S. Taylor, Peter Tertzakian, Tim Wall, Peter Young, Sheldon Harbinson

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2012
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsEncana (Canada)Apache (Canada)
Fundersnot available
KeywordsNatural gasCoalNatural resource economicsWork (physics)Environmental scienceFossil fuelPrimary energyPaceWaste managementPetroleumRenewable natural gasMethaneNatural gas pricesCrude oilFuel gasPetroleum engineeringEngineeringElectricityEconomicsChemistryGeologyCombustion

Abstract

fetched live from OpenAlex

Summary As populations and economies continue to grow globally, energy demand will grow proportionally. Extensive work by Tertzakian (2007, 2009) has shown crude-oil supplies may not keep pace with this increased demand. The shortfall must be met by other energy sources. Only two current energy sources have the global capacity to, by themselves, address increased energy demand in a timely manner. These are natural gas and coal. Traditionally, the major use of crude oil has been for processing into transportation fuels, with lesser amounts being used for petrochemicals and home heating. Natural gas and coal have been used primarily for electrical generation and heating. A pivotal transition will likely occur in which natural gas and coal begin to see increased use as transportation fuels. A battle for market share between primary fuels will likely ensue. The objective of this paper is to present data comparing the environmental impact of using methane vs. coal. A compelling case for the use of natural gas as the future "green fuel" emerges.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.940
Threshold uncertainty score0.955

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.005
GPT teacher head0.218
Teacher spread0.213 · 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 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

Citations5
Published2012
Admission routes1
Has abstractyes

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