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Record W1975345042 · doi:10.1109/mele.2013.2293838

Sensible Transportation Electrification : Get rid of inefficient powertrain designs

2013· article· en· W1975345042 on OpenAlexaff
Randy Reisinger, Ali Emadi

Bibliographic record

VenueIEEE Electrification Magazine · 2013
Typearticle
Languageen
FieldEngineering
TopicElectric and Hybrid Vehicle Technologies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsElectrificationPowertrainAutomotive engineeringGasolineFuel tankEngineeringEnvironmental scienceInternal combustion engineWaste managementElectricityMechanical engineeringElectrical engineering

Abstract

fetched live from OpenAlex

The average internal combustion engine (ICE)-propelled automobile is roughly 10-20% efficient on average at converting the energy in gasoline into forward motion. The remainder of the energy is dissipated into heat or ejected and not fully burned. This means that 80-90% of the fuel is wasted. If you consider an analogy where a person filling the gas tank pumped 1-2 gal into the tank, then pumped 8-9 gal onto the ground, you begin to understand just how much fuel your automobile can waste. This may startle those who might think of their vehicle as clean burning and energy efficient; but, as we will see, the numbers actually get much worse.

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)0.002
metaresearch head score (Gemma)0.004
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: Commentary · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.007
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0210.012

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 head0.013
GPT teacher head0.208
Teacher spread0.195 · 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
GenreCommentary

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

Citations11
Published2013
Admission routes1
Has abstractyes

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