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Record W3114247791 · doi:10.1080/17512549.2020.1863859

Vacuum cleaner as a source of abiotic and biological air pollution in buildings: a review

2020· review· en· W3114247791 on OpenAlexaff
Azad Bahrami, Fariborz Haghighat, Ali Bahloul

Bibliographic record

VenueAdvances in Building Energy Research · 2020
Typereview
Languageen
FieldMedicine
TopicInfection Control and Ventilation
Canadian institutionsInstitut de recherche Robert-Sauvé en santé et en sécurité du travailConcordia University
Fundersnot available
KeywordsVacuum cleanerEnvironmental scienceBioaerosolMistWaste managementIndoor air qualityEnvironmental engineeringAerosolEngineeringChemistryMeteorologyMechanical engineering

Abstract

fetched live from OpenAlex

Vacuum cleaner is known as a proper way to remove settled dust or aerosols from surfaces to protect building occupants against abiotic and biological particles. In fact, the act of vacuuming the surface re-suspends a significant amount of dust and aerosols in the air. The other source of abiotic and biological particles could be the bag of cleaner and the motor of vacuum cleaner. The bag of the cleaners is the reservoir for microorganisms where they can grow, reproduce and become bio-aerosolized in case of penetration through the cleaner filtration system. Micro-organisms can disseminate from the bag, spread in the system and capture on the final filtration system where overshoot airflow can re-entrain the bioaerosol in the breathing zone which will cause catastrophe for all, especially those who are suffering from allergic and infectious diseases. The motor, due to arcing/abrasion of carbon, emits a significant number of nanoparticles, which can target our cardiovascular and respiratory organs. This review presents a summary of studies on vacuum cleaner and its effect on indoor air quality.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.074
GPT teacher head0.446
Teacher spread0.372 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations6
Published2020
Admission routes1
Has abstractyes

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