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Record W2916618808 · doi:10.1016/j.envint.2019.02.035

Profiles and monthly variations of selected volatile organic compounds in indoor air in Canadian homes: Results of Canadian national indoor air survey 2012–2013

2019· article· en· W2916618808 on OpenAlexafffundabout
Yingjie Li, Sabit Cakmak, Jiping Zhu

Bibliographic record

VenueEnvironment International · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicIndoor Air Quality and Microbial Exposure
Canadian institutionsJDSU (Canada)Health Canada
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of CanadaNatural Science Foundation of Shanghai
KeywordsEnvironmental scienceEnvironmental chemistryNonanalHexanalBenzeneVolatile organic compoundIndoor air qualityIndoor airLimoneneEnvironmental engineeringChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

) accounting for more than half (54%) of the sum of all 88 VOCs. The geometric means of the majority of the VOCs obtained from this study were not significantly different from the levels of VOCs that were measured in a previous CHMS data collection cycle between 2009 and 2011. This study also further confirmed higher concentrations of the majority of measured VOCs in apartments compared to houses; and of several tobacco-related VOCs (benzene, styrene, naphthalene, 2-butanone, 2-methyl-1,3-butadiene, 2-furancarboxaldehyde, 2,5-dimethylfuran, benzofuran and phenol) in smoking homes. Furthermore, concentrations of aromatic and aliphatic hydrocarbons were higher in winter and in the months of June and July. Oxygenated hydrocarbons such as aldehydes, alcohols and ketones, on the other hand, were found to be higher in warmer months and peaked in summer months. These results provide valuable information on the levels of selected VOCs of which many have been monitored for the first time in Canadian homes, and can be used to estimate population exposure.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.009
GPT teacher head0.203
Teacher spread0.194 · 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

Citations52
Published2019
Admission routes3
Has abstractyes

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