Composition of Dioxin-like PCBs in Fish: An Application for Risk Assessment
Bibliographic record
Abstract
It is widely accepted that a congener-specific analysis of polychlorinated biphenyls (PCBs), rather than traditional Aroclor equivalent total PCB analysis, is required for risk assessment. This is based on the fact that environmental processes alter the original distribution of PCB congeners in Aroclors and that toxicity varies considerably among the congeners with dioxin-like PCBs (dl-PCBs) generally being among the most toxic. Using the largest known dl-PCB fish dataset, here we present a likely composition of dl-PCBs in fish. In contrast to common perception, we found that the dl-PCB composition is relatively constant (within approximately a factor of 2) regardless of fish species and total PCB level. The abundance of dl-PCBs expressed as a percentage of total PCB (25-75 quartile range) in fish is generally in the order of PCB-118 (3.0-6.2%) > PCB-105 (1.1-2.4%) > PCB-156 (0.39-0.75%) > PCB-167 (0.20-0.43%) > PCB-123 (0.11-0.26%) > PCB-157 (0.09-0.19%) = PCB-114 (0.08-0.18%) > PCB-189 (0.045-0.094%) > PCB-77 (0.018-0.093%) > PCB-126 (0.015-0.036%) > PCB-81 (0.002-0.007%) = PCB-169 (0.001-0.006%). The most toxic dl-PCB congeners PCB-126 and -169 contribute on average only 0.027 and 0.004% of total PCB, respectively. The statistically significant relationships presented between individual di-PCB and total-PCB concentrations can be used as a practical tool to estimate dl-PCBs for risk assessment purposes. A comparison of the dl-PCB pattern presented here with other studies suggests that this dl-PCB composition is applicable to fish from North America and perhaps from other geographical regions throughout the world.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".