Electrifying Change : An Analysis of Streetlight Electric Vehicle Charging Around Multi-Family Residential Units in Vancouver
Bibliographic record
Abstract
Maximizing the percentage of people who can use an electric vehicle is crucial to achieving full electric transportation. Residents living in older multi-family dwellings without access to a personal charging station do not have the facilities to join this electricity powered movement. Public accessible, street-light charging is identified as a solution to this issue. The world is moving towards full electric transportation, however, the emissions from traditional combustion engine vehicles are still contributing to global warming. An increase in electric vehicle usage will lower the carbon footprint of the transportation sector. Geographic Information System (GIS) research determined the ideal street-light electric vehicle charging locations throughout the City of Vancouver by assigning importance weights to areas such as high population density, parking spots, multi-family dwellings, and proximity to public transit. A number of street lights were chosen as the most ideal locations to install level 2 and level 3 electric chargers. Kitsilano and Downtown are representative case studies due to their high population density and electrical infrastructure. A cost estimate of implementing street-light charging stations in Kitsilano and Downtown was determined. The results of this research presents itself as an option for stakeholders to consider when they decide to upgrade the electric transportation infrastructure. Implementing street-light charging stations removes a barrier which has prevented drivers from going electric in the past. Electric vehicle users contribute to the fight against global warming.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".