Bioaccumulation of Polybrominated Diphenyl Ethers and Alternative Halogenated Flame Retardants in a Vegetation–Caribou–Wolf Food Chain of the Canadian Arctic
Bibliographic record
Abstract
The trophodynamics of halogenated flame retardants (HFRs) including polybrominated diphenyl ethers (PBDEs) and alternative HFRs were investigated in the terrestrial, vegetation–caribou–wolf food chain in the Bathurst Region of northern Canada. The greatest concentrations in vegetation (geometric mean of lichens, moss, grasses, willow, and mushrooms) were of the order 2,4,6-tribromophenyl allyl ether (TBP-AE) (10 ng g –1 lw) > BDE47 (5.5 ng g –1 lw) > BDE99 (3.9 ng g –1 lw) > BDE100 (0.82 ng g –1 lw) > 1,2,3,4,5-pentabromobenzene (PBBz) (0.72 ng g –1 lw). Bioconcentration among types of vegetation was consistent, though it was typically greatest in rootless vegetation (lichens, moss). Biomagnification was limited in mammals; only BDE197, BDE206–208 and ∑PBDE biomagnified to caribou from vegetation [biomagnification factors (BMFs) = 2.0–5.1]. Wolves biomagnified BDE28/33, BDE153, BDE154, BDE206, BDE207, and ∑PBDE significantly from caribou (BMFs = 2.9–17) but neither mammal biomagnified any alternative HFRs. Only concentrations of BDE28/33, BDE198, nonaBDEs, and ∑PBDE increased with trophic level, though the magnitude of biomagnification was low relative to legacy, recalcitrant organochlorine contaminants [trophic magnification factors (TMFs) = 1.3–1.8]. Despite bioaccumulation in vegetation and mammals, the contaminants investigated here exhibited limited biomagnification potential and remained at low parts per billion concentrations in wolves.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 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".