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Record W4309538875 · doi:10.30955/gnc2019.00871

Flame Retardants (polybrominated diphenyl ethers, PBDEs) and Organophoshates, OPFRs) In Dust from Canadian Fire Stations

2022· article· en· W4309538875 on OpenAlexaboutno aff
Myrto Petreas, Reber Brown, Darcy Tarrant, Ranjit Singh Gill, Joginder Dhaliwal, Roshni Sarala, Sharyle Patton, June Soo Park

Bibliographic record

VenueGlobal NEST International Conference on Environmental Science & Technology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsnot available
Fundersnot available
KeywordsPolybrominated diphenyl ethersFire retardantEnvironmental chemistryEnvironmental scienceBioaccumulationBrominated flame retardantChemistryPollutant

Abstract

fetched live from OpenAlex

Dust is a good medium to assess indoor exposures to many persistent organics, including flame retardants. With concerns regarding persistence, bioaccumulation and toxicity of many flame retardants, a series of bans and regulations have created shifts in their usage. For firefighters, exposures to flame retardants on and off duty is a high concern. In 2018 we measured flame retardants in Canadian fire station dust and compared our findings with those of our 2015 US fire stations study. We used isotope dilution HRMS for PBDEs and GC-MS/MS for OPFRs. The same flame retardants were present in all stations with high within- and between-station variability. The most prominent among PBDEs was BDE-209, followed by BDE-99. TDCIPP and TPhP were the dominant OPFRs. Overall, data from 2015 (US) and 2018 (Canada) show that OPFRs have surpassed PBDEs in fire station dust, probably reflecting shifts in flame retardant use in consumer products and building materials.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.236
Threshold uncertainty score0.475

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.233
Teacher spread0.224 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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Same venueGlobal NEST International Conference on Environmental Science & TechnologySame topicToxic Organic Pollutants ImpactFrench-language works237,207