Increasing Energy Efficiency in Canada: Highspeed Rail in the Montreal Toronto Corridor
Bibliographic record
Abstract
It is undeniable that anthropogenic carbon emissions are damaging the climate. Accordingly, and within Canada especially, there is a growing demand for technological efforts that improve energy efficiency. One avenue to potentially achieve this is the replacement of airplanes with high speed rails (HSR), specifically between Montreal and Toronto. As such, this paper describes the methodology and results of the two simulation tools, being the "Pathfinding Simulator" and "Socioeconomic Impact Simulator", developed to address the feasibility and impact of such a rail. Notably, the Pathfinding Simulator determined the optimal HSR route to be along the Northern shores of the Saint Lawrence and Lake Ontario, and the Socioeconomic Impact Simulator determined that such a HSR could reduce perpassenger CO2 emissions by almost 1000% (10x) as compared to air travel while maintaining a competitive pricing structure. Cumulatively, this research supports a high speed rail linking Montreal and Toronto due to its improved energy efficiency.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".