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Record W4236448565 · doi:10.32920/ryerson.14654925.v1

Exposing lifestyles: transactive solar architecture

2021· preprint· en· W4236448565 on OpenAlexaff
Michael C. D. Clesle

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsArchitectural engineeringArchitectureRenewable energyEnergy (signal processing)Transactive memorySolar energyEnergy consumptionEnvironmental economicsBusinessComputer scienceEngineeringKnowledge managementElectrical engineeringGeographyEconomics

Abstract

fetched live from OpenAlex

Architecture is capable of increasing the energy consciousness of its occupants through the development of spaces that promote active participation with solar energy systems. Current architectural practice maximizes neither the availability of solar energy nor the potential of solar technologies, failing to recognize critical social elements of renewable energy and address an increasing disconnect between individual energy use and energy source. Through an architectural response, this thesis explores the role of active solar technologies in mediating between occupants, internal and external environments, and energy consumption patterns. The development of an inteactive, energy gathering, animated façade, challenges the perceived 'value' of energy, forcing occupants to measure consumption choices against desires for daylight, views, and the ability to generate income. A transactive environment that makes occupants' lifestyles explicit on a building's façade ensure individuals achieve a heightened sense of energy awareness requiring them to engage their architecture and question overyday energy decisions.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0030.002
Open science0.0000.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.007
GPT teacher head0.185
Teacher spread0.178 · 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 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

Citations1
Published2021
Admission routes1
Has abstractyes

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