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Record W4214510566 · doi:10.1177/0361198106195400104

Motorized Two-Wheeled Vehicle Emissions in India

2006· article· en· W4214510566 on OpenAlexafffund
Madhav G. Badami, Narayan V. Iyer

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2006
Typearticle
Languageen
FieldEngineering
TopicVehicle emissions and performance
Canadian institutionsMcGill University
FundersMcGill University
KeywordsBusinessTransport engineeringAlternative fuel vehicleAir quality indexQuality (philosophy)Environmental economicsEngineeringAutomotive engineeringAlternative fuelsEconomics

Abstract

fetched live from OpenAlex

Motor vehicle activity is growing rapidly in Indian cities, as in other Asian cities, with serious impacts, including deteriorating urban air quality. Motorized two-wheeled (M2W) vehicles, which provide affordable mobility to millions, form the bulk of the motor vehicle fleet and contribute significantly to transport emissions. Vehicle and fuel technologies are important and have been vastly improved since the 1990s. However, on the basis of an in-depth survey of vehicle users and an analysis of emerging trends in consumer preferences, policies, and industry plans, this paper demonstrates various important ways in which user preferences and choices relating to vehicle purchase, operation, and maintenance, interacting with institutional and technological factors, contribute to emissions and affect policy implementation, particularly with reference to M2W vehicles in India. The paper highlights the importance of considering the interaction of these factors, and how users and other actors are affected by and respond to policies, in more effectively addressing emissions from M2W vehicles and other vehicles, especially given in-use realities and constraints.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.177
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.003
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 head0.038
GPT teacher head0.339
Teacher spread0.301 · 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 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

Citations5
Published2006
Admission routes2
Has abstractyes

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