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Record W4245371287 · doi:10.32920/ryerson.14655351

Retrofitting Obsolete Railroad Infrastructure

2021· preprint· en· W4245371287 on OpenAlexaff
Corina Ardeleanu

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicUrban Planning and Landscape Design
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsAmenityRetrofittingContext (archaeology)Public spaceBusinessArchitectural engineeringUrban planningEnvironmental planningTransport engineeringEngineeringCivil engineeringGeographyFinance

Abstract

fetched live from OpenAlex

<p>This thesis will explore the development and design opportunities related to the retrofitting of abandoned railroad corridors in post industrial cities. These lines of infrastructure will be viewed as the lifelines of the city whereby, the ramifications of main transportation arteries will impact the urban network through connectivity and the creation of public open space. This thesis will look at obsolete public railroad infrastructure, as an important fragment of the collective memory of a post-industrial city that can be reactivated to connect back into the transportation urban network. These structures will be identified as landmarks that must be preserved and incorporated into public space and amenity. The reestablishment of the railroad in this context will result in the connection of the contemporary to its past, creating more meaningful and resonant spaces. These transportation corridors will be addressed as part of expanding ecological and man-made systems, thus becoming lifelines of the city, expanding their arteries to feed life into the urban fabric. The natural areas affected by these railroads will be treated as the lungs of the city and made more accessible to the public in order to raise ecological awareness. The railroad thus creates permeability, linking urban and natural areas and reviving its former function of connectivity by re-stitching the urban fabric.</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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.573
Threshold uncertainty score0.988

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 science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0130.000

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 head0.009
GPT teacher head0.207
Teacher spread0.198 · 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 designNot applicable
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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