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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 OpenAlex
Randy Reisinger, Ali Emadi

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

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.

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 score0.160
Threshold uncertainty score0.974

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 studies0.0000.000
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 head0.013
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