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Record W2082138961 · doi:10.1080/02786826.2011.576281

How Particle Resuspension from Inner Surfaces of Ventilation Ducts Affects Indoor Air Quality—A Modeling Analysis

2011· article· en· W2082138961 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAerosol Science and Technology · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsDuct (anatomy)Indoor air qualityAirflowVentilation (architecture)Environmental scienceParticle (ecology)Air quality indexAerosolMechanicsAtmospheric sciencesMeteorologyEnvironmental engineeringEngineeringPhysicsGeologyMechanical engineering

Abstract

fetched live from OpenAlex

Dust particles deposited on the inner surfaces of the ventilation ducts can be resuspended by passing airflow. A physical-science-based model is developed to understand how particle resuspension affects the indoor air quality. This integrated model takes into consideration particle mass balance models for straight ventilation duct, duct bend, ventilation room, and air filter. The straight duct and room models have been validated using experimental data. With the integrated model, we find that in-duct resuspension of particles could lead to significant increase in exposure to airborne particles for indoor occupants. It is also found that indoor particle exposure is a linear function of dust mass loading. Greater ventilation rate, which means higher air speed above the dust particles, would lead to greater exposure ratio, while fresh air ratio has little influence. Possible control methods are discussed as well.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.223
Threshold uncertainty score0.421

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
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 head0.065
GPT teacher head0.298
Teacher spread0.233 · 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