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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 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

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.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.974
Threshold uncertainty score0.760

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.074
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