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Record W4248122505 · doi:10.32920/ryerson.14644446.v1

Creating place with palimpsest.

2021· preprint· en· W4248122505 on OpenAlexaffabout
Jeffrey T. Cheung

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicArchitecture, Modernity, and Design
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPalimpsestArchitectureIdentity (music)Architectural engineeringTypologyComputer scienceAestheticsHistoryArchaeologyEngineeringArt

Abstract

fetched live from OpenAlex

With the notion of globalization affecting the architecture of cities all over the world, architectural identity becomes lost in what seems like a homogenous cityscape of similar buildings. Considering the removal, and erasure, of buildings, new proposals should incorporate elements of the local architecture along with new implementations. Design utilizing principles of palimpsest will allow for the integration of the present construction methods while also complimenting the existing surrounding identity. This idea of palimpsest will combine Toronto’s historic layers with the ideas of identity to create an architecture that establishes a new ‘sense of place’ to juxtapose the past with the present. Strategies, that demonstrate the concept of palimpsest, will be applied to the Victory Soya Mills Silos; a structure that is located within one of many layers of Toronto’s waterfront history. A design proposal that works with the existing silos can create a unique situation informing and utilizing palimpsest to create a strong identity and ‘sense of place.’ The design of a library, in conjunction with the silos, will further enhance the idea of palimpsest combining a historic neighborhood of Toronto with a program that represents an evolving building typology. The success of this project clarifies that the concept of palimpsest can be applied towards an old structure to work with a part of identity to create a ‘sense of place’.

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 categoriesnone
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 score0.780
Threshold uncertainty score0.976

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.222
Teacher spread0.204 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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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