MétaCan
Menu
Back to cohort
Record W4211148017 · doi:10.32920/ryerson.14663517

Park-scape infrastructure

2021· preprint· en· W4211148017 on OpenAlexaffabout
Jordan Emmanuel Breccia

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicUrban Design and Spatial Analysis
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsAmenityPedestrianPublic spacePlan (archaeology)GeographyPublic domainSpace (punctuation)Public transportTransport engineeringUrban planningScale (ratio)ArchitectureArchitectural engineeringEnvironmental planningCivil engineeringBusinessEngineeringComputer scienceCartographyArchaeology

Abstract

fetched live from OpenAlex

This thesis is predicated on the objectives of Toronto's Official Plan: the cessation of outward suburban development, increased growth in the urban core, increased public amenity space to support this greater density, reduced presence of the car, and the support of public transit, walking and cycling. Recognition of the serious lack of available public domain within Toronto's core to provide the appropriate scale of public space required to support this new level of density, is the subject of this thesis proposal. This thesis proposed a new large scale, north to south linear park for Toronto that integrates a system of bicycle, jogging, and pedestrian paths that connect it into existing mass transit systems, the urban core, and the existing east west park system, the Martin Goodman Trail. It creates this new public space by re-appropriating several vehicular lanes of Jarvis Street, and proposing the integration of new building development, park, sidewalk and street to amalgamate the space needed to accomplish this. Building and landscape, private and public space overlap and interconnect, to complete this new seamless urban park.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.126
Threshold uncertainty score0.252

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0640.009

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 head0.006
GPT teacher head0.176
Teacher spread0.170 · 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 designTheoretical or conceptual
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

Explore more

Same topicUrban Design and Spatial AnalysisFrench-language works237,207