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Record W2068379943 · doi:10.1177/0270467606292150

Alternative Fuels in Transportation

2006· article· en· W2068379943 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBulletin of Science Technology & Society · 2006
Typearticle
Languageen
FieldEngineering
TopicElectric Vehicles and Infrastructure
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFossil fuelRenewable energyNatural resource economicsGreenhouse gasRenewable fuelsEnergy transitionEnvironmental scienceAlternative energyEnvironmental economicsInvestment (military)Energy developmentBusinessWaste managementEconomicsEngineeringEcology

Abstract

fetched live from OpenAlex

The realization of dwindling fossil fuel supplies and their adverse environmental impacts has accelerated research and development activities in the domain of renewable energy sources and technologies. Global energy demand is expected to rise during the next few decades, and the majority of today's energy is based on fossil fuels. Alternative energy sources and technologies can play a vital role in lowering or eliminating our reliance on fossil fuels. However, such a transition will require a large investment and will not be reversible. The benefits of hydrogen and other liquid fuels as transportation fuels are not marginal, but the real urgency in greenhouse gas reductions and fossil fuel replacement should not be translated into an energy infrastructure based on unsustainable sources in the long run. It is wise to examine each option thoroughly and objectively now and discard those with little potential so our focus and effort may be placed elsewhere.

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.

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 score0.066
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.003
GPT teacher head0.192
Teacher spread0.189 · 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