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Record W2973293137

City of Vancouver EV Infrastructure Strategy Report

2016· article· en· W2973293137 on OpenAlexaboutno aff
Dahlia Garas, Gustavo O Collantes, Michael Nicholas

Bibliographic record

VenueeScholarship (California Digital Library) · 2016
Typearticle
Languageen
FieldEngineering
TopicElectric Vehicles and Infrastructure
Canadian institutionsnot available
Fundersnot available
KeywordsIncentiveGovernment (linguistics)BusinessLocal governmentIncentive programInstallationPublic administrationEngineeringEconomicsPolitical scienceMarket economy
DOInot available

Abstract

fetched live from OpenAlex

The role of the local government in supporting the growth and maintenance of a strong plug‐in electric vehicle market in Vancouver is evaluated in this report. This report identifies areas of action in which a local government, such as Vancouver, can impact their region based on a thorough understanding of the current plug‐in vehicle market, international demonstration projects, and research efforts. Specifically, workplace and public charging is needed to reinforce and fulfill the gaps from home‐based charging in dense urban regions. Local government can encourage investments in workplace and public charging by providing clear regional guidelines for installers and customers, providing appropriate incentives to businesses, allowing for an innovative marketplace in the vehicle charging industry, and collaborating with the regional utility to identify specific opportunities for optimization and encouragement of utility rates and vehicle‐grid interactions.

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.156
Threshold uncertainty score0.315

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.000
Scholarly communication0.0050.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0610.017

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 head0.185
Teacher spread0.179 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations7
Published2016
Admission routes1
Has abstractyes

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