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Record W2163769818 · doi:10.5539/ep.v2n3p81

Energy and Environmental Impacts of Urban Buses and Passenger Cars–Comparative Analysis of Sensitivity to Driving Conditions

2013· article· en· W2163769818 on OpenAlexvenueno aff
Leonid Tartakovsky, M. Gutman, Doron Popescu, M. Shapiro

Bibliographic record

VenueEnvironment and Pollution · 2013
Typearticle
Languageen
FieldEngineering
TopicVehicle emissions and performance
Canadian institutionsnot available
Fundersnot available
KeywordsOccupancyTransport engineeringEnvironmental impact assessmentPassenger transportEnergy consumptionEnvironmental scienceRoad transportService (business)Environmental analysisAutomotive engineeringEngineeringCivil engineeringBusiness

Abstract

fetched live from OpenAlex

A methodology is suggested for a comparative analysis of energy and environmental impacts of various urban transport modes. A total emission indicator is used as a tool for integral assessment of vehicle emissions. An environmental impact factor is suggested in order to compare between the various transport modes that use different energy sources. Vehicle occupancy values, yielding equality of the specific environmental impact factors and specific energy consumption of the compared transport modes are used for the analysis purposes. This methodology is applied for a comparison between the buses and the passenger cars at various levels of service, road gradients and urban road types. The comparison results reveal that the environmental impact of the bus for driving at an urban access road falls below the one of the passenger car when the bus occupancy is 14–18 persons. Urban buses turn out to be energetically beneficial over passenger cars at occupancy values substantially lower compared with those providing a similar environmental impact.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.006
GPT teacher head0.194
Teacher spread0.188 · 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 designObservational
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

Citations4
Published2013
Admission routes1
Has abstractyes

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