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Record W4282001015 · doi:10.24904/footbridge2022.289

Two Modern Crossings in Historic Canadian Sites: Creating "Places for People"?

2022· article· en· W4282001015 on OpenAlexaboutno aff
Mark Langridge

Bibliographic record

VenueFootbridge 2022, Madrid: Creating Experience · 2022
Typearticle
Languageen
FieldEngineering
TopicStructural Engineering and Vibration Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Dominance (genetics)Character (mathematics)Expression (computer science)Intervention (counseling)AestheticsHistorySociologyVisual artsArtPsychologyComputer scienceArchaeology

Abstract

fetched live from OpenAlex

<p>One of the most challenging design problems of our time is how to achieve an appropriate aesthetic expression for a new bridge intervention in an important historic context, and also make it into an inviting public space. Mark Langridge has led the architectural and aesthetic design of many high profile bridge crossings in Canadian heritage settings over the past twenty years that attempt to achieve this balance.</p><p>This paper looks at two recent completed projects in important historic places, <b>Flora Footbridge </b>in Ottawa and <b>Garrison Crossing </b>in Toronto, and describes how the designs attempt to become successful “places for people”. It includes conceptual renderings and post-completion photos, showing how people are now using the crossings through Canada’s varied seasons.</p><p>In both cases it was imperative that the new structure respect and not overwhelm the character of the <i>heritage place</i>, yet it was also very important that it be an expression of its own time. The concept of <i>minimal intervention </i>is critical in this context – the heritage character-defining elements should retain visual dominance within the setting, with new interventions presenting a secondary, understated visual expression that does not diminish the original historic character, and over time, should ideally enhance it.</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 categoriesMeta-epidemiology (narrow)
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 score0.039
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
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.243
Teacher spread0.231 · 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
Published2022
Admission routes1
Has abstractyes

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