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Record W4255911025 · doi:10.32920/ryerson.14655849

Suburban / urban grafting: towards an architectural and urban hybrid

2021· preprint· en· W4255911025 on OpenAlexaff
John Kotsampouikidis

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicUrban Design and Spatial Analysis
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsUrbanityRecreationGeographyArchitectural engineeringEnvironmental planningUrban designUrban landscapeResource (disambiguation)Urban planningTransport engineeringCivil engineeringComputer scienceEngineeringPolitical science

Abstract

fetched live from OpenAlex

Land is a finite resource which does not support the low density suburban model on a large scale. The alternative, the urban condition, does not satisfy the needs of families. The goal is to define a middle ground, to create a hybrid, by taking the best qualities of the suburbs and urb to develop a new vision for the city. Is it possible to combine the typological characteristics and spatial qualities of the suburbs in an urban format where the disadvantages of the suburbs are mitigated by the multiplicity of urbanity? For the purpose of this thesis the design proposal is tested on Ijburg, Amsterdam, the Netherlands. The resultant hybrid is manifested through the mixing of different housing typologies both suburban and urban in conjunction with and elevated street which provides private access to individual units and public access to recreational and leisure facilities.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.726
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.014
GPT teacher head0.208
Teacher spread0.195 · 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 designSimulation or modeling
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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