Spatial Distribution and Air–Water Exchange of Organic Flame Retardants in the Lower Great Lakes
Bibliographic record
Abstract
Organic flame retardants (OFRs) such as polybrominated diphenyl ethers (PBDEs) and novel halogenated flame retardants (NHFRs) are ubiquitous, persistent, and bioaccumulative contaminants that have been used in consumer goods to slow combustion. In this study, polyethylene passive samplers (PEs) were deployed throughout the lower Great Lakes (Lake Erie and Lake Ontario) to measure OFRs in air and water, calculate air-water exchange fluxes, and investigate spatial trends. Dissolved Σ12BDE was greatest in Lake Ontario near Toronto (18 pg/L), whereas gaseous Σ12BDE was greatest on the southern shoreline of Lake Erie (11 pg/m(3)). NHFRs were generally below detection limits. Air-water exchange was dominated by absorption of BDEs 47 and 99, ranging from -964 pg/m(2)/day to -30 pg/m(2)/day. Σ12BDE in air and water was significantly correlated with surrounding population density, suggesting that phased-out PBDEs continued to be emitted from population centers along the Great Lakes shoreline in 2012. Correlation with dissolved Σ12BDE was strongest when considering population within 25 km while correlation with gaseous Σ12BDE was strongest when using population within 3 km to the south of each site. Bayesian kriging was used to predict dissolved Σ12BDE over the lakes, illustrating the utility of relatively highly spatially resolved measurements in identifying potential hot spots for future study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".