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Record W2618266008

Reductions in residential wood smoke concentrations and infiltration efficiency using electrostatic air cleaner interventions

2011· article· en· W2618266008 on OpenAlexaff
Amanda J. Wheeler, Mark Gibson, Tony Ward, Ryan W. Allen, Judy Guernsey, Matt Seaboyer, James Kuchta, Richard H. Gould, Dave Stieb

Bibliographic record

Venue12th International Conference on Indoor Air Quality and Climate 2011 · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsSimon Fraser UniversityDalhousie UniversityHealth Canada
Fundersnot available
KeywordsEnvironmental scienceInfiltration (HVAC)Indoor air qualityAir quality indexParticulatesEnvironmental engineeringAir filtrationFiltration (mathematics)Waste managementEngineeringMeteorologyChemistryMathematics
DOInot available

Abstract

fetched live from OpenAlex

Residential woodsmoke (RWS) has received increasing attention as an important source of ambient particulate matter (PM2.5) that negatively impacts air quality and health. An investigation of the impact of ambient RWS emissions on indoor air quality was conducted in 32 residences, together with an evaluation of the effectiveness of electrostatic air cleaners (ESAC) at reducing indoor PM2.5 concentrations. Monitoring was conducted for 3 days in total. On day 1 the woodstove operated as usual with no ESAC. On days 2 and 3 the woodstove was not in operation. The ESAC was randomly chosen to operate in “filtration” or “placebo filtration” mode on day 2 and then switched on day 3. Twenty-one homes had valid infiltration efficiency estimates on the two days when indoor woodstoves were not in use. Average infiltration efficiencies were reduced from 0.49 (Std Dev = 0.29) to 0.29 (Std Dev = 0.20) when the air cleaner was in operation.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.218
GPT teacher head0.397
Teacher spread0.178 · 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

Citations4
Published2011
Admission routes1
Has abstractyes

Explore more

Same venue12th International Conference on Indoor Air Quality and Climate 2011→Same topicAir Quality and Health Impacts→French-language works237,207→