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Record W2057243126 · doi:10.1260/1478-0771.9.1.77

Architectronics: Towards a Responsive Environment

2011· article· en· W2057243126 on OpenAlexaff
AnnaLisa Meyboom, Greg Johnson, J Wójtowicz

Bibliographic record

VenueInternational Journal of Architectural Computing · 2011
Typearticle
Languageen
FieldEngineering
TopicArchitecture and Computational Design
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsArchitectureScope (computer science)MechatronicsField (mathematics)Systems engineeringEngineeringComputer scienceArchitectural engineeringControl (management)Control engineeringArtificial intelligenceGeography

Abstract

fetched live from OpenAlex

Contemporary architecture can be seen as a dynamic system that changes in response to its environment and even as a system that can modify itself. Interactive or responsive environments are not totally new to architecture; however, the possibilities in architecture have only begun to be examined. To look at the possibilities in this emerging field experimentation is required and the architect must develop an understanding of the language of sensors, actuators and control systems. This article examines an interdisciplinary design research studio with mechatronic engineers which allowed a wide range of experimentation. It shows that the scope of what can be done with responsive architecture is hard to imagine from where we now stand and that it is only through a broad range of experimentation that we can find the most beneficial uses of this powerful technology. The resulting projects - kinetic architecture on control systems - challenge our understanding of what our built environment could be.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.007
Scholarly communication0.0060.008
Open science0.0020.008
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.002

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 head0.016
GPT teacher head0.214
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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations13
Published2011
Admission routes1
Has abstractyes

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