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Record W4289782023 · doi:10.1371/journal.pone.0268341

Development of an observational exposure human biomonitoring study to assess Canadian children’s DEET exposure during protective use

2022· article· en· W4289782023 on OpenAlexafffundabout
Jennifer C. Gibson, Leonora Marro, Michael M. Borghese, Danielle Brandow, Lauren Remedios, Mandy Fisher, Morie Malowany, Katarzyna Kieliszkiewicz, Anna O. Lukina, Kim Irwin

Bibliographic record

VenuePLoS ONE · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPesticide Exposure and Toxicity
Canadian institutionsHealth Canada
FundersHealth CanadaPublic Health AgencyPublic Health Agency of Canada
KeywordsDEETInsect repellentBiomonitoringContext (archaeology)UrineToxicologyObservational studyMetaboliteMedicineEnvironmental healthBiologyChemistryEnvironmental chemistryInternal medicine

Abstract

fetched live from OpenAlex

Biomonitoring data of N,N-diethyl-meta-toluamide (DEET) in children is scarce and limited to controlled exposure and surveillance studies. We conducted a 24-hour observational exposure and human biomonitoring study designed to estimate use of and exposure to DEET-based insect repellents by Canadian children in an overnight summer camp setting. Here, we present our study design and methodology. In 2019, children between the ages of 7 and 13 took part in the study (n = 126). Children controlled their use of DEET-based insect repellents, and provided an account of their activities at camp that could impact insect repellent absorption. Children provided a total of 389 urine samples throughout the study day, and reported the time that they applied insect repellent, which allowed us to contextualize urinary DEET and metabolite concentrations with respect to the timing of insect repellent application. DEET (2.3% <Limits of detection (LOD)) and two metabolites, N,N-diethyl-m-(hydroxymethyl)benzamide (DHMB) (0% <LOD) and 3-diethylcarbamoyl benzoic acid (DCBA) (0%

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.037
Threshold uncertainty score0.133

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0040.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
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 head0.183
GPT teacher head0.272
Teacher spread0.090 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2022
Admission routes3
Has abstractyes

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Same venuePLoS ONESame topicPesticide Exposure and ToxicityFrench-language works237,207