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Record W4392914129 · doi:10.32920/25412704

Indoor Soundscape and Natural Ventilation in Residential Buildings

2024· preprint· en· W4392914129 on OpenAlexaff
Keziah Folarin-Babatunde

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsSoundscapeDoorsNatural ventilationArchitectural engineeringContext (archaeology)EnclosureVentilation (architecture)Window (computing)Natural (archaeology)Noise (video)LimitingComputer scienceEnvironmental scienceAcousticsEngineeringSound (geography)TelecommunicationsGeographyArtificial intelligenceMechanical engineering

Abstract

fetched live from OpenAlex

Buildings play an important role in defining the boundaries between the exterior and interior environment. Ventilation openings such as operable windows, patio doors, skylights etc. constitute the enclosure’s connection between the inside and outside. The connection they provide has been identified as a limiting factor in the use of natural ventilation this is due to the conflict between ventilation needs of the users and the intrusion of external noise. Indoor soundscape focuses on how the occupants perceive, experience, and understand the indoor acoustic environment in the context of their activities. Using soundscape research methodology combined with quantitative and qualitative data collection methods, sound measurements were carried out in the residences of sixteen participants under closed and opened window conditions. Based on the research findings from the combined data sources, recommendations for the application of passive noise control strategies were made to provide favourable indoor soundscape for occupants while enjoying the benefits of natural ventilation under opened window conditions.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.023
GPT teacher head0.397
Teacher spread0.374 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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