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Record W2121990058 · doi:10.24908/pceea.v0i0.3958

REDUCING GLOBAL WARMING THROUGH ADVANCED VEHICLE DESIGN

2011· article· en· W2121990058 on OpenAlexaffvenue
R. L. Evans

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2011
Typearticle
Languageen
FieldEngineering
TopicElectric Vehicles and Infrastructure
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPropulsionCombustionMiles per gallon gasoline equivalentAutomotive engineeringFossil fuelGlobal warmingInternal combustion engineEnvironmental scienceRange (aeronautics)Green vehicleBattery electric vehicleFuel efficiencyElectric vehicleEngineeringPower (physics)Waste managementClimate changeAerospace engineeringGeology

Abstract

fetched live from OpenAlex

Global warming has been identified as one of the most important problems facing mankind in the 21st century. Currently, some 6 gigatonnes of CO2 are emitted each year as a result of the combustion of fossil fuels, and a large fraction of these emissions originate from the transportation sector. By examining the complete energy conversion chain, the choice of primary energy source for any particular application becomes easier to understand. A discussion of alternatives to the internal combustion engine as the sole power source for vehicular propulsion is presented, and some form of hybrid electric vehicle propulsion system is identified as being a likely choice to reduce fossil fuel consumption, and therefore CO2 emissions from the transportation sector. The demonstrated market success of grid-independent hybrid vehicles may be followed by a new design of “plug-in hybrid” vehicles in which it is possible to travel for up to 100 km in an all-electric mode, while maintaining the option of using an internal combustion engine when greater range between charging cycles is required.

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: Other · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0060.002

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 head0.008
GPT teacher head0.189
Teacher spread0.181 · 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
GenreOther

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

Citations0
Published2011
Admission routes2
Has abstractyes

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Same venueProceedings of the Canadian Engineering Education Association (CEEA)Same topicElectric Vehicles and InfrastructureFrench-language works237,207