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Record W2974786587 · doi:10.1002/9781119097921.ch6

Near Future Developments: Advances in Simulation and Real‐Time Feedback

2018· other· en· W2974786587 on OpenAlexaff
Terri Peters

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEngineering
TopicArchitecture and Computational Design
Canadian institutionsMicrosemi (Canada)
Fundersnot available
KeywordsMainstreamArchitectureComputer scienceContext (archaeology)Relevance (law)FlourishingProcess (computing)Test (biology)Human–computer interactionRelation (database)Inclusion (mineral)Architectural engineeringEngineeringPsychologyVisual arts

Abstract

fetched live from OpenAlex

This chapter discusses three overlapping and multi-disciplinary themes in the architectural design process, including real-time feedback, human behavior as a computational data source, and reconsiderations of comfort and experience to consider gradients of performance. The chapter highlights projects that are experimental structures pointing to promising new trajectories for 'computing the environment' that offer perspectives not seen in mainstream practice. These projects are part of a larger movement in architecture, both in school and in practice, to design and build 1:1 prototypes and pavilions that serve as influential test beds for new ideas. The impact of these temporary pavilions and pop-ups has been studied in recent architectural essays, for the relevance they hold in relation to larger architectural ideas, in the context of digital design and virtual spaces, and these reveal a plurality of approaches. Architect Philippe Rahm is known for his experimental architectural proposals that push the boundaries of environmental design. The increasing ease and speed of gaining feedback from physical and virtual testing enables new ways of designing. Real-time feedback, design for interaction with the environment, not only measuring or simulating its behavior, and the inclusion of new metrics like sound, are flourishing in experimental projects, and are likely to come to mainstream practice in the near future.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.586
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.217
Teacher spread0.214 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2018
Admission routes1
Has abstractyes

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