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ROBO studio: towards architectronics

2010· article· en· W2800315451 on OpenAlexaff
AnnaLisa Meyboom, J Wójtowicz, Greg Johnson

Bibliographic record

VenueProceedings of the International Conference on Computer-Aided Architectural Design Research in Asia · 2010
Typearticle
Languageen
FieldEngineering
TopicArchitecture and Computational Design
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsArchitectureMechatronicsFraming (construction)Computer scienceStudioSystems engineeringApplications architectureSystems architectureEngineeringArchitectural engineeringHuman–computer interactionArtificial intelligence

Abstract

fetched live from OpenAlex

Contemporary architecture can be seen as a dynamic system that causes change to its environment, or even as system that can modify itself. Interactive or responsive environments are not totally new to architecture however the possibilities in architecture have only been lightly referred to. This interdisciplinary design studio, with mechatronics engineers and architects collaborating, explored possible applications with real world equipment, sensors and knowledge. Development of responsive architecture requires architects to have a fluency in sensors, actuators and their control system programming. New potential application of technologies requires a re-framing of what that technology could do in a different social application. Together these issues challenged architecture and engineering students in a collaborative design environment. 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)0.006
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.033
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0020.004
Scholarly communication0.0090.007
Open science0.0030.007
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0330.020

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 head0.081
GPT teacher head0.322
Teacher spread0.241 · 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 designBench or experimental
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

Citations6
Published2010
Admission routes1
Has abstractyes

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