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Record W2752182245 · doi:10.3923/rjes.2017.18.28

Limits Imposed by the Second Law of Thermodynamics on Reducing Greenhouse Gas Emissions to the Atmosphere

2017· article· en· W2752182245 on OpenAlexaff
M.A. Rosen, Richard Berthiaume

Bibliographic record

VenueResearch Journal of Environmental Sciences · 2017
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsGreenhouse gasAtmosphere (unit)Environmental scienceSecond law of thermodynamicsThermodynamicsAtmospheric sciencesPhysicsGeology

Abstract

fetched live from OpenAlex

Background and Objective: Society is moving toward unconventional oil and gas exploitation and greenhouse gas emissions reduction.The main objective of this research is to develop an exergy based conceptual framework that integrates all aspects of fuel production from resource extraction to environmental protection, including CO 2 abatement.Materials and Methods: A multistep deterministic method, from Carnot to exergy analysis, is used to develop a conceptual framework with the help of information from the literature.The approach used in this study mirrors some initial steps in the development of exergy analysis that can be found in Carnotʼs work, but the focus is on resource flow rather than maximum work or efficiency.The hypothesis that the flow of fossil resources to produce fuel is related to source-sink properties explored with a simple case related to heat transfer.The possible transposition of this simple case to fuel production is examined to provide insights into the possible uses of the conceptual development.Results: This paper proposes an exergy based conceptual source-sink approach, following Carnotʼs theory, to understand the complex problem of fuel production in relation to CO 2 abatement.The source is defined by natural resources and their associated quality and one sink is the atmosphere or any CO 2 confinement option.Conclusion: The proposed conceptual framework for source-sink exergy analysis of resource flow could improve our understanding of the thermodynamic limits and the sustainability of energy and material production.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.006
Scholarly communication0.0020.004
Open science0.0010.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.037
GPT teacher head0.338
Teacher spread0.301 · 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 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

Citations4
Published2017
Admission routes1
Has abstractyes

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