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Record W2111034336 · doi:10.1098/rsta.2008.0143

Carbon neutral hydrocarbons

2008· review· en· W2111034336 on OpenAlexaff
Frank Zeman, David W. Keith

Bibliographic record

VenuePhilosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · 2008
Typereview
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsGreenhouse gasEnvironmental scienceBiomass (ecology)Carbon neutralityCarbon dioxideBio-energy with carbon capture and storageCarbon-neutral fuelBiofuelCarbon fibersAtmospheric carbon cycleEnergy carrierClimate change mitigationCarbon dioxide in Earth's atmosphereSoftware deploymentCarbon sequestrationNatural resource economicsWaste managementHydrogenChemistrySyngasMaterials scienceEcologyComputer science

Abstract

fetched live from OpenAlex

Reducing greenhouse gas emissions from the transportation sector may be the most difficult aspect of climate change mitigation. We suggest that carbon neutral hydrocarbons (CNHCs) offer an alternative pathway for deep emission cuts that complement the use of decarbonized energy carriers. Such fuels are synthesized from atmospheric carbon dioxide (CO2) and carbon neutral hydrogen. The result is a liquid fuel compatible with the existing transportation infrastructure and therefore capable of a gradual deployment with minimum supply disruption. Capturing the atmospheric CO2 can be accomplished using biomass or industrial methods referred to as air capture. The viability of biomass fuels is strongly dependent on the environmental impacts of biomass production. Strong constraints on land use may favour the use of air capture. We conclude that CNHCs may be a viable alternative to hydrogen or conventional biofuels and warrant a comparable level of research effort and support.

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: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.005

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 head0.033
GPT teacher head0.284
Teacher spread0.251 · 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
GenreReview

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

Citations107
Published2008
Admission routes1
Has abstractyes

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