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Record W3001048030 · doi:10.14305/ibpc.2018.hf-1.01

A picture is worth a thousand words: Smartphone photograph-based surveys for collecting data on office occupant adaptive opportunities

2018· article· en· W3001048030 on OpenAlexaff
William O’Brien, Anthony T. Fuller, Marcel Schweiker, Julia K. Day

Bibliographic record

VenueHealthy, Intelligent and Resilient Buildings and Urban Environments · 2018
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsCarleton University
Fundersnot available
KeywordsUsabilityData collectionComputer scienceSample (material)Post-occupancy evaluationHuman–computer interactionInterface (matter)Architectural engineeringApplied psychologyMultimediaEngineeringPsychology

Abstract

fetched live from OpenAlex

In the past several decades, psychological aspects have been become important to holistic building occupant comfort and satisfaction evaluations. Psychological dimensions of comfort include occupants’ opportunities to interact with their indoor environment and perceived control over the indoor environment. Current post-occupancy evaluations tend to focus on collecting quantitative data, despite overwhelming evidence that contextual factors can profoundly impact occupant comfort. This paper proposes and tests a novel method for data collection to study adaptive comfort opportunities. A smartphone-based survey was developed to concurrently collect office occupants’ subjective evaluations of usability and comfort of spaces, in addition to photographs of all key building interfaces. The photos were coded to obtain quantitative characteristics of offices, such as whether the interface is obstructed. With a sample of 39 office workers, this paper reveals the effectiveness of this novel photographbased survey method, while also providing some initial quantitative and qualitative results.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.305
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.059
GPT teacher head0.266
Teacher spread0.207 · 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 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

Citations0
Published2018
Admission routes1
Has abstractyes

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