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Record W2138622173

An internet-based stated choices household survey for alternative fuelled vehicles

2007· article· en· W2138622173 on OpenAlexaffabout
Dimitris Potoglou, Pavlos Kanaroglou

Bibliographic record

VenueORCA Online Research @Cardiff (Cardiff University) · 2007
Typearticle
Languageen
FieldEnergy
TopicEnergy, Environment, and Transportation Policies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAlternative fuel vehicleContext (archaeology)Metropolitan areaSustainabilityIncentiveEnvironmental economicsThe InternetQuality (philosophy)Transport engineeringBusinessEconomicsEngineeringComputer scienceAlternative fuels
DOInot available

Abstract

fetched live from OpenAlex

The development of alternative fuelled vehicle technology is a key strategy towards environmental sustainability and improved air quality in cities. Analysis of the role of vehicle technology in fulfilling sustainability targets requires estimates of future vehicle demand. The inability to observe actual car-type preferences for cleaner vehicles has led researchers to the development of stated choice methods. This paper reports on the design and descriptive analysis of a stated choices survey on the demand for alternative fuelled vehicles in the Census Metropolitan Area of Hamilton, conducted through the Internet. Respondents were asked to select the vehicle they would most likely buy out of a set of conventional, hybrid and alternative-fuel options over a time horizon of five years. Characteristics such as vehicle purchase price, fuel and maintenance cost, acceleration, alternative fuel incentives, fuel availability and pollution levels were used to describe each vehicle presented. To our knowledge, this is the first study of its kind that focuses at the urban level and the Canadian context and also, it is the first to demonstrate the time- and cost-efficiency of the Internet in designing and collecting Stated Choices data for automobile demand.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.523
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.089
GPT teacher head0.337
Teacher spread0.248 · 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

Citations9
Published2007
Admission routes2
Has abstractyes

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