Modelling the Spatial Distribution of Hybrid-Electric Vehicles in Windsor, Ontario
Bibliographic record
Abstract
Technological advancements in recent years have allowed hybrid electric vehicles (HEV) to improve in terms of fuel efficiency and associated tailpipe emissions. As a result, the demand for HEVs in Canada has been on the rise but the market share is still negligible. In this paper, the population of HEVs in the Windsor Census Metropolitan Area (CMA), Ontario, Canada for the year 2011 is considered to study the determinants that led to the observed spatial distribution of this class of vehicles across the different census tracts within the CMA. Vehicle-specific characteristics along with locational variables are employed in the analysis. Locational factors are based on census tract profile attributes (namely socio-economic factors) that were acquired from the 2011 Census National Household Survey (NHS). Other variables used in the analysis include mixed density index and land use heterogeneity. Discrete choice modeling is used to explain the probability of finding HEVs in a specific area. Moran’s I statistics analysis suggests that the spatial distribution of HEVs exhibits a clustering pattern. Locational factors pertaining to the education, type of job, income, age, size of household of the people living in an area where HEVs were observed explain the spatial prevalence of various classes of HEVs. Also, areas with mixed density and heterogeneous land uses tend to give rise to the existence of HEVs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".