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Record W3201288184 · doi:10.1111/ina.12932

A national survey of window‐opening behavior in United States homes

2021· article· en· W3201288184 on OpenAlexaboutno aff
Glenn Morrison, John G. Cagle, Gauri Date

Bibliographic record

VenueIndoor Air · 2021
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsnot available
FundersU.S. Environmental Protection Agency
KeywordsWindow (computing)DemographicsQuarter (Canadian coin)GeographyAir conditioningEnvironmental scienceAir quality indexDemographyMeteorologyPhysical geographyEngineeringArchaeology

Abstract

fetched live from OpenAlex

Air exchange is among the most important building parameters influencing indoor air quality and energy use. Over 18-month period we surveyed over 3800 individuals to generate a contemporary, nationwide measure of window- and door-opening behavior. We also identified influences of demographics, climate, and region. For the entire survey, including all seasons and geographic regions, 43.9% of respondents said that at least one window was open the day prior to taking the survey. Greater window-opening frequency was associated with having a lower income, living in attached homes or apartments, renting, lack of air conditioning, or being Asian or Hispanic. People living in the west and north open windows considerably more frequently and longer than those in the southeastern US. Window-opening frequency and duration increases with outdoor temperature until a maximum occurs at 18-21°C. At temperatures greater than this, window frequency decreases. The pattern roughly holds, by region, with the peak occurring at a lower temperature in the NW (12°C), and a higher temperature in the SW and SE. The frequency of door opening is roughly half that of window opening with similar, but not identical, demographic, regional, and climate associations.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.753
Threshold uncertainty score0.259

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.020
GPT teacher head0.245
Teacher spread0.225 · 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 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

Citations19
Published2021
Admission routes1
Has abstractyes

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