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Record W3212951739 · doi:10.32920/ryerson.14658192.v1

Toronto’s apartment tower renewal initiative: infusing value into surplus lands for community development

2021· preprint· en· W3212951739 on OpenAlexaffabout
Frederick Thibeault

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicUnderground infrastructure and sustainability
Canadian institutionsToronto Metropolitan UniversityUniversity of Waterloo
Fundersnot available
KeywordsApartmentTowerInfillSubdivisionValue (mathematics)BusinessEnvironmental planningEconomic growthNatural resource economicsEngineeringEconomicsGeographyCivil engineering

Abstract

fetched live from OpenAlex

Toronto is home to over 1,189 apartment towers built between 1945 and 1984, following LeCorbusier’s “tower in the park” model. Today, many apartment towers communities are fraught with issues that demand immediate and focused attention. Several towers are now approaching 50 years of age, and are beginning to show signs of decay, neglect, and decline, presenting concerns surrounding their physical condition, environmental impacts, and access to essential amenities within close proximity. The former Mayor of Toronto David Miller responded by initiating a study to identify solutions to growing concerns, and financing strategies to achieve it. Notwithstanding these issues, tower neighbourhoods have access to an exorbitant amounts of surplus lands that could accommodate infill activities, and spur investments in these neighbourhoods. The goal of this paper is to assess if surplus lands can be leveraged as the primary funding source to finance the goals and objectives of Toronto’s tower renewal initiative.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.447
Threshold uncertainty score0.899

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0040.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.020
GPT teacher head0.266
Teacher spread0.245 · 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 designQualitative
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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