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Record W2909204097 · doi:10.1016/j.envpol.2019.01.027

Flame retardants in urban air: A case study in Toronto targeting distinct source sectors

2019· article· en· W2909204097 on OpenAlexafffundabout
Amandeep Saini, Jenna Clarke, Narumol Jariyasopit, Cassandra Rauert, Jasmin K. Schuster, Sabina Halappanavar, Greg J. Evans, Yushan Su, Tom Harner

Bibliographic record

VenueEnvironmental Pollution · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsMinistry of the Environment, Conservation and ParksHealth CanadaUniversity of TorontoEnvironment and Climate Change Canada
FundersClean Air Regulatory Agenda
KeywordsPolybrominated diphenyl ethersEnvironmental scienceVolatilisationEnvironmental chemistryPassive samplingDominance (genetics)OrganophosphateFire retardantPollutantPesticideChemistryEcologyBiology

Abstract

fetched live from OpenAlex

). The atmospheric concentrations of given chemical classes also showed a general trend of lower levels in winter as compared to summer months. Statistically significant negative correlations between the natural logarithm of concentrations and inverse of temperature for some OPEs and PBDEs highlighted the role of volatilization from local sources at given sites as primarily influencing their atmospheric concentrations. Overall, this study adds to the current knowledge of urban settings as a major emitter of the chemicals of emerging concern and their replacements, as well as the ongoing problem of phased out PBDEs due to their presence in existing inventories of commercial/recycled products. It is recommended that long-term monitoring programs targeting flame retardants (FRs) include urban sites, which provide an early indicator of effectiveness of control measures of targeted FRs, while at the same time providing information on emission sources and trends of replacement FR chemicals.

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 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.005
GPT teacher head0.216
Teacher spread0.211 · 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 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

Citations63
Published2019
Admission routes3
Has abstractyes

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