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Record W2087903107 · doi:10.1177/0270467602022004004

Fuel Cell Cars: Panacea or Pipe Dream?

2002· article· en· W2087903107 on OpenAlexaff
Shahram Karimi, F. R. Foulkes

Bibliographic record

VenueBulletin of Science Technology & Society · 2002
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHydrogen vehicleHydrogen fuel enhancementBrake specific fuel consumptionCombustionHydrogen fuelInternal combustion engineHydrogen economyAutomotive engineeringFuel efficiencyEnvironmental scienceFuel cellsHydrogenAuxiliary power unitGreen vehicleWaste managementEngineeringChemistryElectrical engineering

Abstract

fetched live from OpenAlex

Hydrogen fuel cells are likely to begin replacing conventional internal combustion engines as a power generation method for transportation applications in the near future. A life cycle analysis of a hydrogen fuel cell was performed to examine the major environmental impacts of such an engine in comparison with an internal combustion engine. To quantify the emissions, material consumption and energy consumption were identified by carrying out mass and energy balances, respectively. Wherever possible, a “well-to-wheel” approach was adopted to identify all the processes involved. The size of the hydrogen fuel cell engine selected was 60 kW, which would power a small automobile weighing around 800 kg. This report briefly describes the materials and processes involved in assembling such an engine, with their respective environmental impacts. Different technologies to build a hydrogen economy also are discussed because hydrogen is an integral part of most fuel cell engines. The main conclusion is that if an environmentally sustainable system of hydrogen production is found, the use of hydrogen and, in turn, hydrogen fuel cell cars would be highly beneficial. Thus, the theoretical potential of fuel cells is great for environmental benefits, but practical applications might prove otherwise.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.314
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.006
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.014
GPT teacher head0.216
Teacher spread0.202 · 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.

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
Published2002
Admission routes1
Has abstractyes

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