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Impact of varying area of polluting surface materials on perceived air quality

2003· article· en· W2147391527 on OpenAlexafffund
W. Sakr, Henrik Nellemose Knudsen, Lars Gunnarsen, Fariborz Haghighat

Bibliographic record

VenueIndoor Air · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsConcordia University
FundersConcordia University
KeywordsDanishAir quality indexIndoor air qualityCivil engineeringKnudsen numberEngineeringEnvironmental researchEnvironmental scienceArchitectural engineeringGeographyEnvironmental planningEnvironmental engineeringMeteorology

Abstract

fetched live from OpenAlex

A laboratory study was performed to investigate the impact of the concentration of pollutants in the air on emissions from building materials. Building materials were placed in ventilated test chambers. The experimental set-up allowed the concentration of pollution in the exhaust air to be changed either by diluting exhaust air with clean air (changing the dilution factor) or by varying the area of the material inside the chamber when keeping the ventilation rate constant (changing the area factor). Four different building materials and three combinations of two or three building materials were studied in ventilated small-scale test chambers. Each individual material and three of their combinations were examined at four different dilution factors and four different area factors. An untrained panel of 23 subjects assessed the air quality from the chambers. The results show that a certain increase in dilution improves the perceived air quality more than a similar decrease in area. The reason for this may be that the emission rate of odorous pollutants increases when the concentration in the chamber decreases. The results demonstrate that, in some cases the effect of increased ventilation on the air quality may be less than expected from a simple dilution model.

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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.536
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.066
GPT teacher head0.353
Teacher spread0.287 · 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 teacher head, not a consensus.

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

Citations10
Published2003
Admission routes2
Has abstractyes

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