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Record W3139764619 · doi:10.21577/0100-4042.20170741

OESTADODAARTE DE COMPOSTOS CARBONÍLICOSVOLÁTEISEMAMBIENTES INTERNOS:IMPACTOSÀ SAÚDEEMETODOLOGIAS DEAMOSTRAGEMEANÁLISES

2021· article· en· W3139764619 on OpenAlexaff
Murilo de Oliveira Souza, Hellen Gonçalves Vieira, Benigno Sánchez, Maria Canela

Bibliographic record

VenueQuímica Nova · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicIndoor Air Quality and Microbial Exposure
Canadian institutionsCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal
Fundersnot available
KeywordsDerivatizationEnvironmental scienceAdsorptionEnvironmental chemistryCartridgeChemistryHealth riskWaste managementChromatographyMaterials scienceOrganic chemistryEngineeringHigh-performance liquid chromatographyEnvironmental health

Abstract

fetched live from OpenAlex

THE STATE OF THE ART OF VOLATILE CARBONYL COMPOUNDS IN INTERNAL ENVIRONMENTS: IMPACTS TO HEALTH AND SAMPLING METHODOLOGIES AND ANALYSIS. The primary sources of emission of volatile carbonyl compounds (CCs) in indoor environments are chipboard panels, laminate floors, plywood, paints and solvents, household products, fiberglass, gas stoves, heaters and heating systems. Several studies have already confirmed an indoor/outdoor (I/O) ratio greater than 1 (one) for several CCs, indicating that these compounds are emitted mainly from internal sources. CCs (especially aldehydes and ketones) are easily absorbed into the airways, presenting a considerable mutagenic, teratogenic and carcinogenic risk to humans. Therefore, this study aimed to review the CCs found in different indoor environments, the effects and impacts of these compounds on human health and the national and international guidelines that establish their maximum exposure limits. The methodologies most used in the literature to analyze CCs in the air were reviewed directly (in real-time) or indirectly (using pre-treatment steps). A detailed review of the sampling techniques: with and without adsorption, with and without derivatization, with adsorption and derivatization simultaneously; using various methodologies employed during the last decades (cartridges, filters, tubes or liquid absorbent-impinger) was carried out. Finally, this work describes the instrumental methods and the advantages and disadvantages of determining CCs individually and simultaneously in the atmosphere.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.227
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0070.001

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 head0.028
GPT teacher head0.268
Teacher spread0.240 · 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; both teacher heads agree on what is shown here.

Study designBench or experimental
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
Published2021
Admission routes1
Has abstractyes

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