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Record W2734992730

Electrifying demand: Increasing zero emission vehicle adoption in Vancouver

2017· article· en· W2734992730 on OpenAlexaboutno aff
Molly J. Henry

Bibliographic record

VenueSummit (Simon Fraser University) · 2017
Typearticle
Languageen
FieldEngineering
TopicElectric Vehicles and Infrastructure
Canadian institutionsnot available
Fundersnot available
KeywordsZero emissionZero (linguistics)BusinessEconomicsEngineeringElectrical engineering
DOInot available

Abstract

fetched live from OpenAlex

Light duty vehicles account for approximately one-third of Vancouver’s annual greenhouse gas emissions. To reduce these emissions, Vancouver has committed to transition to 100 percent renewable energy for all light duty transportation in the city by 2050. However, the cost difference between zero emission vehicles and the dominant internal combustion engine is identified as a barrier to adoption for many consumers. This study examines how municipal policy can minimize this difference. Key considerations are identified through interviews with experts and a jurisdiction scan of three cities. Four policy options are assessed against criteria of effectiveness, public acceptability, government cost, and administrative complexity. An education campaign and discounted parking are recommended for immediate implementation, and further analysis should be done on the development of a toll zone. At the same time, mode-shifting away from private vehicles to active transportation and public transit should remain a top policy priority.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.652
Threshold uncertainty score0.798

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.007
GPT teacher head0.192
Teacher spread0.185 · 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.

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

Citations1
Published2017
Admission routes1
Has abstractyes

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