Ecological factors contributing to variability of persistent organic pollutant bioaccumulation within forage fish communities of the Detroit River, Ontario, Canada
Bibliographic record
Abstract
Understanding variability of contaminant bioaccumulation within and among fish populations is critical for distinguishing between the chemical and biological mechanisms that contribute to food web biomagnification and quantifying contaminant exposure risks in aquatic ecosystems. The present study examined the relative contributions of chemical hydrophobicity (octanol-water partition coefficient [KOW ]) and habitat use as factors regulating variability in polychlorinated biphenyl (PCB) congener bioaccumulation in 3 lower trophic level cyprinid species across spatial and temporal scales. Bluntnose minnows (Pimephales notatus), spottail shiners (Notropis hudsonius), and emerald shiners (Notropis atherinoides) were sampled at 3 locations in the Detroit River, Ontario, Canada. Variability in PCB concentration was evaluated with respect to several factors, including chemical hydrophobicity, site, season, species, and weight using sum of squares and Levene's test of homogeneity of variance. Individual variability in bioaccumulated congener-specific residues depended on chemical hydrophobicity with mid- and high-range KOW congeners (log KOW >6.0), demonstrating the highest amount of variance compared with low KOW congeners. Different feeding strategies also contributed to the variance observed for mid-range KOW congeners among species. In the present study, benthic feeding specialists exhibited lower variance in PCB concentrations compared with the 2 generalist species. The results indicate that chemical hydrophobicity and feeding ecology not only contribute to differences in the biomagnification potentials of fish, but also regulate between-individual variation in PCB concentrations both across and within fish species.
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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 | 0.001 |
| 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 | 0.001 |
| 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".