Trophic transfer of flame retardants (PBDEs) in the food web of Lake Erie
Bibliographic record
Abstract
We studied the occurrence, bioaccumulation, and biomagnification of polybrominated diphenyl ethers (PBDEs) in a mixed food web of native and non-native species in Lake Erie. Non-native species were found at the basal level of the web (dreissenid mussels), at the intermediate level (round gobies, rainbow smelt), and at the top predator rung (steelhead trout). Mean concentrations of total PBDEs in biota (wet mass) ranged from 1.03 ng·g–1 in dreissenid mussels to 31.5 ng·g–1 in walleye. Large piscivores (smallmouth bass, steelhead trout, walleye, and lake trout) had PBDE concentrations three to seven times higher than prey fish (emerald shiners, round gobies, rainbow smelt, and yellow perch). Walleye had the highest concentration of PBDEs among all of the fish species analyzed. BDE 47 was the dominant congener found in biota. Biomagnification factors (corrected for trophic level) indicated that total PBDEs were biomagnified in three fish species: rainbow smelt, smallmouth bass, and steelhead trout. Overall, BDEs 47 and 100 had the highest level of trophic magnification (TMF) from invertebrates to top predators. For fish species, the highest TMF was for BDEs 47 and 49+71. We found that dreissenid mussels and round gobies had the lowest PBDE contamination of the organisms analyzed; however, other non-native prey species such as rainbow smelt contributed significantly to the biomagnification of PBDEs.
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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 | 0.001 |
| 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".