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Record W4401923168 · doi:10.5772/intechopen.1005318

Advanced Sustainable Logistics with HSR for the Development in Great Montreal Area

2024· book-chapter· en· W4401923168 on OpenAlexaffabout
Yonglin Ren, Anjali Awasthi

Bibliographic record

VenueIntechOpen eBooks · 2024
Typebook-chapter
Languageen
FieldEngineering
TopicUrban and Freight Transport Logistics
Canadian institutionsMcGill UniversityConcordia University
Fundersnot available
KeywordsSustainable developmentBusinessVolume (thermodynamics)Transport engineeringIndustrial organizationEngineeringPolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.958
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.200
Teacher spread0.174 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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