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Record W4382394965 · doi:10.32920/23593239

A Thick Green Line: Extracting Public Space from Infrastructure

2023· preprint· en· W4382394965 on OpenAlexaboutno aff
Cheryl Atkinson

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicUrban Design and Spatial Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsAmenityPublic spacePedestrianDowntownGeographyRealmPopulationSpace (punctuation)Urban sprawlTransport engineeringUrban designArchitectural engineeringRegional scienceUrban planningCivil engineeringCartographyEngineeringSociologyPolitical scienceComputer science

Abstract

fetched live from OpenAlex

<p>The speed and intensity of the current residential high-rise development of Toronto’s downtown core is unprecedented in North America. While this new population has significantly rejuvenated the street life and economy of this area, the public space to support it remains severely inadequate in quality, extent, and connectivity. This design proposal looks at the opportunities available to extract new public domain from existing remnant infrastructure within the historic core. By analyzing the urban design history of this area, and current pedestrian and traffic patterns, a new intervention is proposed that re-allocates space from cars to people. This proposal creates new amenity space and links a number of vestigial and derelict historic public squares into a larger comprehensive system. The proposed linear park establishes a needed cultural, civic, and community focus and encourages walking and cycling for this live-work population. The design is made present by a hyper-articulation of its horizontal surface—a repetitive and continuous paving with a memorable pattern and form. This strategy is a pragmatic but aggressive approach that recognizes the criticality of extracting a pedestrian realm within the emerging hyper-density of this particular car-oriented city. It makes use of available historic patterns, and existing redundant traffic infrastructure, to create place, meaning, and amenity.</p> <p> </p>

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.793
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 integrity0.0010.001
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 head0.035
GPT teacher head0.230
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
Published2023
Admission routes1
Has abstractyes

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