Spatial and Temporal Trends (2004–2016) of Selected Alternative Flame Retardants in Fish of the Laurentian Great Lakes
Bibliographic record
Abstract
Following the phase-out of polybrominated diphenyl ether (PBDE) and hexabromocyclododecane (HBCDD) flame retardants (FRs) from North American markets, the use of alternative FRs has increased. In this study the occurrence and spatiotemporal distributions of 18 dechlorane analogues (collectively referred to as DECs) and 20 alternative brominated FRs (referred to as ABFRs, i.e., brominated FRs other than PBDEs and HBCDD) were investigated in top predator fish megacomposites (i.e., a composite of all 50 fish) collected yearly from each of the Great Lakes from 2004 to 2016. Frequently detected substances include dechlorane 602, 603, 604 Component B, anti- and syn-dechlorane plus, and chlordene plus, as well as several brominated benzene FRs (i.e., hexabromobenzene, pentabromotoluene, and tetrabromo- o-chlorotoluene). Concentrations of ΣDECs and ΣABFRs ranged from 0.33-31.9 ng/g lipid weight (lw) (0.01-8.3 ng/g wet weight or ww) and 0.91-54.7 ng/g lw (0.09-7.1 ng/g ww), respectively. Flame retardant contamination exhibited chemical-specific spatial variations across the five lakes. Concentrations of ΣABFRs in Lake Erie fish were generally lower than those from other lakes. By contrast, fish ΣDEC concentrations were highest in Lake Ontario and the composition of dechlorane analogues differed significantly between Lake Ontario and the other lakes, indicating likely point-source influences. Temporal analyses revealed declining trends of ΣDECs and ΣABFRs in most lakes except Lake Erie, with age corrected trend slopes of -13.5% to -8.8% and -20.1% to -7.0% per year, respectively.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".