Contrasting PCB bioaccumulation patterns among Lake Huron lake trout reflect basin-specific ecology
Bibliographic record
Abstract
This study collected multiple age classes of lake trout from Lake Huron's Main Basin, Georgian Bay, and North Channel regions to compare and contrast top predator polychlorinated biphenyl (PCB) bioaccumulation patterns in separate compartments of the same ecosystem. Sum PCB concentrations were highest for Main Basin (260 ± 24.9 ng g(-1) wet wt) fish, followed by Georgian Bay (74.6 ± 16.2 ng g(-1) ) and North Channel (42.0 ± 3.3 ng g(-1)) fish. Discriminant functions analysis of lake trout PCB profiles and stable carbon (δ(13)C) and nitrogen (δ(15)N) isotope values clearly distinguished fish by location, indicating high degrees of basin fidelity throughout their lifetimes in addition to highly contrasting PCB bioaccumulation profiles. These unique profiles were not attributable to significant differences in lake trout lipid contents (p = 0.856) or trophic position (δ(15)N; p = 0.334), with rainbow smelt representing the primary prey across the basins. Furthermore, significant differences were observed among the basins for the relationships between PCB biomagnification factors and hydrophobicity. An empirical model for predicting PCB biomagnification in Lake Huron lake trout indicated that basin-specific population growth rates and prey abundances were significant for explaining these contrasting patterns of PCB bioaccumulation. The results of the present study are fundamental for understanding the role of ecology in legacy persistent organic pollutant (POP) bioaccumulation. Specifically, ecosystem characteristics such as prey abundances, foraging ecology, and ultimately consumer growth can regulate the variability of legacy POP bioaccumulation as observed within and among a wide range of freshwater ecosystems.
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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 | 0.000 |
| 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) | 0.000 | 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".