Halogenated Flame Retardants in Predator and Prey Fish From the Laurentian Great Lakes: Age-Dependent Accumulation and Trophic Transfer
Bibliographic record
Abstract
The identification, persistence, accumulation and trophic transfer of 25 polybrominated diphenyl ether (PBDE) congeners, 23 non-PBDE halogenated flame retardants (NPHFRs), 4 polybrominated-diphenoxybenzenes (PB-DiPhOBzs) and 6 methoxylated (MeO−) PB-DiPhOBzs were investigated in predator and prey fish collected in 2010 from sites in the North American Great Lakes of Ontario ( n = 26) and Erie ( n = 39). Regardless of locations or species, 20 PBDEs and 12 NPHFRs were quantifiable in at least one of the 65 analyzed samples, and polybrominated-1,4-diphenoxybenzenes (PB-DiPhOBzs) and MeO-PB-DiPhOBzs were not detectable in any of analyzed samples. Among the FRs, the greatest concentrations were the ∑PBDE, ranging from 1.06 (Rainbow Smelt, Lake Erie) to 162 (Lake Trout, Lake Ontario) ng/g wet weight (ww), which was followed by mean HBCDD concentrations ranging ND to 17.3 (Lake Trout, Lake Ontario) ng/g ww. The remaining FRs were generally not detectable or at sub-ppb levels. In most of cases, FR concentrations in samples from Lake Ontario were greater than those from Lake Erie. Strong and significant positive linear relationships occurred between log-normalized FR concentrations (ww or lipid weight (lw)) and ages of the top predator Lake Trout ( n = 16, from Lake Ontario), and the estimated FR doubling ages ( T 2 ) were 2.9–6.4 years. For Walleye from Lake Erie, significantly positive linear relationships were also observed for some FRs, but the linear relationships generally became negative after FR concentrations were normalized with lipid weight. This study provides novel information on FR accumulation in aquatic organisms, and for the first time, significant positive linear relationships are reported between log-normalized FR concentrations (lw or ww) and ages of Lake Trout from the Great Lakes.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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".