MétaCan
Menu
Back to cohort
Record W2327548166 · doi:10.1021/es403055e

Nationally Representative Levels of Selected Volatile Organic Compounds in Canadian Residential Indoor Air: Population-Based Survey

2013· article· en· W2327548166 on OpenAlexafffundabout
Jiping Zhu, Suzy L Wong, Sabit Cakmak

Bibliographic record

VenueEnvironmental Science & Technology · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicIndoor Air Quality and Microbial Exposure
Canadian institutionsStatistics CanadaHealth Canada
FundersHealth CanadaAustralian Government
KeywordsEnvironmental scienceEnvironmental healthIndoor air qualityIndoor airVolatile organic compoundPopulationEnvironmental chemistryEnvironmental engineeringMedicineChemistry

Abstract

fetched live from OpenAlex

A comprehensive, population-based national indoor air survey was conducted in 2009-2011 in Canada. A total of 84 volatile organic carbons (VOCs) from 3218 houses, 546 apartments, and 93 other dwelling types were measured using passive sampling followed by thermal desorption GC/MS. A total of 12 VOCs were measured in both this study and the 1992 Canadian national study. Arithmetic means of VOCs in this study were 2-5 times lower than those in the 1992 study with the exception of a higher styrene level (1.13 μg · m(-3)). Comparing the geometric means of the 24 VOCs showed that levels for the VOCs in this study were comparable to those reported in Europe. They were generally within a factor of 2; 1,4-dichlorobenzene (0.21 μg · m(-3)) and 1,2,4-trimethylbenzene (0.51 μg · m(-3)) were noticeably lower in this study than in the European studies. There were 47 VOCs detected in more than 50% of Canadian households; 33 of them were higher in houses than in apartments for all nonsmoking homes, while only 4 were lower in houses than in apartments. A total of 11 of 47 VOCs were higher in smoking homes compared to nonsmoking homes, while the rest had similar levels. Principal component analysis identified several groups of VOCs with possible common sources.

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 score0.234
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.008
GPT teacher head0.230
Teacher spread0.221 · 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

Citations99
Published2013
Admission routes3
Has abstractyes

Explore more

Same venueEnvironmental Science & TechnologySame topicIndoor Air Quality and Microbial ExposureFrench-language works237,207