An archetypal routing network model to help identify potential charging locations for long-haul electric vehicles in Ontario, Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Some estimates show long-haul transport trucks contribute as much as 10% of all Canada’s greenhouse gas emissions. Long-haul electric vehicles (LHEVs) or “electric big rigs” offer a potentially compelling option to mitigate these emissions. However, LHEV charging is expected to burden the power grid significantly more than charging smaller passenger electric vehicles. To date, there is very little research on the impact of charging such vehicles on power grids. The following study leverages conventional long-haul truck GPS data to develop an archetypal routing network (ARN) model that can help identify candidate charging infrastructure locations in Ontario, Canada. Results suggest that based on historical LHEV travel patterns, most candidate charging station locations fall along critical road links in Ontario like Highway 401 and Highway 400. Subsequently, the additional electricity demand of these stations is estimated and compared with Ontario’s current electricity demand. Though the charging stations’ aggregate daily demand is smaller than Ontario’s overall demand, some of these stations’ hourly electricity demand during peak hours are great enough to put significant pressure on local infrastructures.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it