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Record W4250954243 · doi:10.32920/ryerson.14648565

Experiential Density: Infill strategies within Toronto's Centres - Bringing Human Scale, Character, and Walkability

2021· preprint· en· W4250954243 on OpenAlexaffabout
Dustin Lee Sauder

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicUrban Design and Spatial Analysis
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsInfillWalkabilityApartmentPedestrianArchitectural engineeringRealmGeographyExperiential learningPublic spaceUrban sprawlUrban planningPerceptionBuilt environmentEconomic geographyCivil engineeringSociologyEngineeringArchaeologyPsychology

Abstract

fetched live from OpenAlex

A variety of approaches are being used to accommodate Toronto’s growing population. Most of these solutions rely on high-rise and mid-rise developments, emphasizing the quantity of density instead of the quality. However, this thesis focuses on blocks of slab towers, and explores how the perception of an environment and intensity of development can form an experiential density. Introducing new public and pedestrian orientated spaces to the neglected land between apartment towers to improve the experience of urban blocks also offers open space to increase the density in Toronto’s designated growth areas. To achieve this new environment, urban blocks containing clusters of slab towers will be fragmented into walkable distances; scaled outdoor spaces will activate the neglected park; infill will increase density and define outdoor spaces; and transitional areas will mediate the public and private realm, all to bring life into the block and improve experience of urban density.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.536
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.217
Teacher spread0.208 · 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 designBench or experimental
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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