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Record W4241809551 · doi:10.32920/ryerson.14655489

Light rail transit as a tool for urban brownfield revitalization

2021· preprint· en· W4241809551 on OpenAlexaff
Davin McCully

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsBrownfieldUrban sprawlEnvironmental planningBusinessInvestment (military)SustainabilityTransport engineeringUrban planningRail transitUrban regenerationPublic transportGeographyCivil engineeringEngineeringPolitical scienceRedevelopment

Abstract

fetched live from OpenAlex

In Europe and in North America, Light Rail Transit (LRT) is increasingly being seen as a viable and attractive transportation option which is not as cost prohibitive as heavy rail, yet carries more passengers and travels at higher speeds than traditional bus transit. Brownfield regeneration is at the forefront of urban land use policy, as cities try to reign in sprawl and address local economic, social, and environmental implications of such underused or abandoned sites. This paper will examine the relationship between the implementation of LRT in urban environments, and how that investment in transportation infrastructure affects the regeneration of urban brownfield sites. This will be achieved through the use of three urban case studies, each with subpopulations between 100,000 – 500,000. Key Words: Light Rail Transit, Brownfield, Transportation, Sustainability, Urban Mobility, Urban Financing, Municipal Plans and Policies.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.000

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 head0.028
GPT teacher head0.311
Teacher spread0.283 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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
Published2021
Admission routes1
Has abstractyes

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