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Record W2910339641

Occupant-adaptive façade interaction: relationships and conflicts

2018· article· en· W2910339641 on OpenAlexaff
Alessandra Luna-Navarro, Roel Loonen, Shady Attia, Miren Juaristi, Aurora Monge-Barrio, Mattia Donato, R. Rabensheifer, Mauro Overend

Bibliographic record

VenueOpen Repository and Bibliography (University of Liège) · 2018
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsArup Group (Canada)
Fundersnot available
KeywordsFacadeDaylightArchitectural engineeringComputer scienceDomain (mathematical analysis)Human–computer interactionInteraction designEngineeringCivil engineering
DOInot available

Abstract

fetched live from OpenAlex

Adaptive or Intelligent façades are those that can interact with users and dynamically vary their performance or properties (controlling thermal or solar energy, air flow and/or daylight) in response to changing external conditions and indoor demands. Consequently, adaptive façades could help to ensure occupant comfort, health, well-being and satisfaction, while allowing resource-efficient building operation. However, effective adaptive façade solutions that provide an optimal balance between user comfort, satisfaction and energy efficiency cannot be achieved without knowledge of the multidisciplinary complexity of the user-façade interaction. The main objective of this paper is to preliminary review and analyse the existing literature on user interaction with intelligent buildings,<br/>especially with façades, and to propose a conceptual framework to capture the multi-disciplinary and multi-domain complexity of user interaction with adaptive façades. The interaction between adaptive façades and occupants is then modelled as a closed loop of information and action exchange. This paper concludes indicating which are the future research needs to be addressed in order to define what is a satisfactory interaction strategy between occupants and façades.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.329

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.029
GPT teacher head0.213
Teacher spread0.184 · 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 designObservational
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

Citations3
Published2018
Admission routes1
Has abstractyes

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