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Record W2901277054 · doi:10.1080/09603123.2018.1543799

Factors associated with plasma concentrations of polychlorinated biphenyls (PCBs) and dichlorodiphenyldichloroethylene (p,p’-DDE) in the Canadian population

2018· article· en· W2901277054 on OpenAlexafffundabout
Kavita Singh, Subramanian Karthikeyan, Djordje Vladisavljevic, Annie St-Amand, Hing Man Chan

Bibliographic record

VenueInternational Journal of Environmental Health Research · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsHealth CanadaUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of CanadaHealth CanadaCanada Research ChairsCanadian Institutes of Health ResearchUniversité du Québec en OutaouaisUniversity of Ottawa
KeywordsBiomonitoringPopulationAnthropometryEnvironmental healthMedicinePhysiologyDemographyEnvironmental chemistryChemistryInternal medicine

Abstract

fetched live from OpenAlex

This study describes blood plasma concentrations of PCBs and p,p’-DDE in the Canadian population aged 20–79 years. PCBs and p,p’-DDE were measured in 1668 participants in the Canadian Health Measures Survey, Cycle 1 (2007–2009). We investigated how concentrations vary by sociodemographic, anthropometric, and lifestyle variables, identified factors associated with exposures, and evaluated concentrations against health-based guidance values. Congeners of PCB most commonly detected were PCB-138, PCB-153, and PCB-180. p,p’-DDE was detectable in > 99% of the samples. Factors associated with ∑PCBs were age, region of birth, frequency of fish consumption, and liver intake (R2 = 58.1%). For p,p’-DDE, significant factors were sex, age, region of birth, household education, and ethnic origin (R2 = 47.0%). PCB concentrations in Canadians were similar to those in the United States, and lower than those reported in Europe. A small percentage equalled or exceeded the Human Biomonitoring value of 3.5 µg/L for PCBs. Few exceedances of the p,p’-DDE biomonitoring equivalent were observed.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.173
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.091
GPT teacher head0.367
Teacher spread0.276 · 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.

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

Citations17
Published2018
Admission routes3
Has abstractyes

Explore more

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