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Record W4230764888 · doi:10.32920/ryerson.14649372

(Re)discovering Toronto's waterfront: infrastructure and connectivity in a post-industrial landscape

2021· preprint· en· W4230764888 on OpenAlexaffabout
Geordie Gordon

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicMaritime Ports and Logistics
Canadian institutionsToronto Metropolitan UniversityUniversity of Victoria
Fundersnot available
KeywordsRedevelopmentRelocationContext (archaeology)Work (physics)Environmental planningLand useBusinessProcess (computing)Civil engineeringTransport engineeringGeographyEngineeringComputer science

Abstract

fetched live from OpenAlex

The transition of waterfront land use from industrial to post-industrial is a global phenomenon. There are several forces that are driving this change, including the advancement of shipping technology and the relocation of industrial processes to areas with greater availability of land. In place of industrial uses, many cities have undertaken, or are in the process of undertaking the redevelopment of their waterfront. As a result of past industrial use, there often exists, a significant amount of transportation infrastructure that isolates the city from the waterfront. This paper establishes the context for waterfront redevelopment, before examining the impact of infrastructure urban forms by using the work of Kevin Lynch as a tool for analysis. Several case precedents are used to examine the course of action that other North American cities have pursued to mitigate the impact of infrastructure forms on the waterfront and how they may influence the way Toronto deals with its waterfront infrastructure.

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: Empirical
Teacher disagreement score0.247
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)0.0030.000

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 head0.011
GPT teacher head0.206
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 routes2
Has abstractyes

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