Advanced Sustainable Logistics with HSR for the Development in Great Montreal Area
Bibliographic record
Abstract
Currently, the new transportation tool High-Speed Railway (HSR) pushes economic and social development to a great level in some countries. Because of its high speed (actual speed 430 km/h, experiment speed 600–1200 km/h) and high efficiency, it makes good transportation in a surprisingly quick increment and then supports supply chain logistics running at a greatly higher level than that before. Especially, a stimulation of trade volume will happen due to the increased speed of transportation within the HSR network. The success of HSR in the Asia area implies its future application may produce an economic engine in East Canada or the Great Montreal Area with extended regions, which will stimulate the local economic and social development in an excellent model. Especially for the goods flow or trade volume, the implementation of the HSR network centred in the Great Montreal Area can bring to the community. This chapter will make a mathematical model deviated from the Gravity Model to investigate the relationship between the goods flow and the HSR speed. The research on their relationship demonstrated that the HSR would be able to substitute the low-speed vehicle style and increase economic development.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.030 | 0.006 |
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".