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Record W4386524500 · doi:10.32920/24101496.v1

Convergence of Architectural, Visual and Climate Data

2023· preprint· en· W4386524500 on OpenAlexaff
Filiz Klassen

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicArchitecture and Computational Design
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsArchitectural engineeringComputer scienceArchitectureContext (archaeology)VisualizationElectric lightTechnological convergenceRenewable energyData scienceMeteorologyEngineeringVisual artsGeographyArtificial intelligenceTelecommunications

Abstract

fetched live from OpenAlex

<p>The relationship between buildings and image, both static and moving, has occupied a significant role throughout architectural history. Current innovations in materials research, digital technology and lighting are strengthening this connection as a vital idea permeating image arts and built spaces. The objective of this paper is to expand the intersection of architecture and image within the context of climate change. As the recent kinetic art and media facades as well as the author’s own prototypes highlight, this emergent practice promotes climate conscious interactive systems and sustainable illumination that are integrated into building surfaces. While responding to changes in environmental variables and illuminating the building with renewable energy sources, this alternative approach also emphasizes developing a social and environmental narrative for the use of media facades, challenging the current application for mainly commercial advertising. The author’s own climate responsive prototypes reveal the methodological experimentation with weather elements of snow, rain, light and wind. The three-tiered display of collected climactic information lead to integration of various technologies to make the elements visible, harvest and generate energy from their kinetic movement. This paper will present conclusions drawn from the previous experimentation, as well as a new direction to eliminate additional light pollution while integrating architectural and climate visualization with the sustainable media façade technology in built environments. </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 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 score0.523
Threshold uncertainty score0.503

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 integrity0.0000.000
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 head0.055
GPT teacher head0.292
Teacher spread0.238 · 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
Published2023
Admission routes1
Has abstractyes

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