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Record W4256096095 · doi:10.32920/ryerson.14648289

Walkable live/work neighbourhoods in big box greyfields

2021· preprint· en· W4256096095 on OpenAlexaffabout
David Keith Bryan Platt

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicUrban Planning and Landscape Design
Canadian institutionsToronto Metropolitan UniversityKingston Health Sciences CentreThornhill Medical (Canada)
Fundersnot available
KeywordsRedevelopmentNeighbourhood (mathematics)New UrbanismWork (physics)WalkabilityUrban designPedestrianSustainabilityUrban planningGeographyTransport engineeringEnvironmental planningBusinessArchitectural engineeringUrbanismCivil engineeringEngineeringBuilt environmentArchitecture

Abstract

fetched live from OpenAlex

The research focussed on how urban greyfields, especially abandoned big box retail, can be redeveloped into mixed-use walkable live/work neighbourhoods for an energy-efficient future. The history of shopping centre and big box retail and the mixed-use residential redevelopment of such sites using the new LEED Neighbourhood Development rating system were studied. Four principle concepts were found and used to guide the design projects. They were sustainable urbanism planning versus Modernist auto-dependency; mixed-use planning; walkable neighbourhood concepts; and live/work units. These help create local employment in transit-based neighbourhoods having nearby services and amenities to reduce commuting and auto-dependency. The design project on a 25-acre Toronto greyfield includes Continuous Productive Urban Landscapes for food production and a trail system promoting walking, cycling and greater contact with nature. Greyfield sites used for sustainable communities help offset valuable farmland losses and offer useful urban intensification possibilities for a looming energy crisis.

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), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.399
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 science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0170.001

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 head0.019
GPT teacher head0.214
Teacher spread0.196 · 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; both teacher heads agree on what is shown here.

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 routes2
Has abstractyes

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