Evaluation and Interconversion of Various Indicator PCB Schemes for ∑PCB and Dioxin-Like PCB Toxic Equivalent Levels in Fish
Bibliographic record
Abstract
Polychlorinated biphenyls (PCBs) remain chemicals of concern more than three decades after the ban on their production. Technical mixture-based total PCB measurements are unreliable due to weathering and degradation, while detailed full congener specific measurements can be time-consuming and costly for large studies. Measurements using a subset of indicator PCBs (iPCBs) have been considered appropriate; however, inclusion of different PCB congeners in various iPCB schemes makes it challenging to readily compare data. Here, using an extensive data set, we examine the performance of existing iPCB3 (PCB 138, 153, and 180), iPCB6 (iPCB3 plus 28, 52, and 101) and iPCB7 (iPCB6 plus 118) schemes, and new iPCB schemes in estimating total of PCB congeners (∑PCB) and dioxin-like PCB toxic equivalent (dlPCB-TEQ) concentrations in sport fish fillets and the whole body of juvenile fish. The coefficients of determination (R(2)) for regressions conducted using logarithmically transformed data suggest that inclusion of an increased number of PCBs in an iPCB improves relationship with ∑PCB but not dlPCB-TEQs. Overall, novel iPCB3 (PCB 95, 118, and 153), iPCB4 (iPCB3 plus 138) and iPCB5 (iPCB4 plus 110) presented in this study and existing iPCB6 and iPCB7 are the most optimal indicators, while the current iPCB3 should be avoided. Measurement of ∑PCB based on a more detailed analysis (50+ congeners) is also overall a good approach for assessing PCB contamination and to track PCB origin in fish. Relationships among the existing and new iPCB schemes have been presented to facilitate their interconversion. The iPCB6 equiv levels for the 6.5 and 10 pg/g benchmarks of dlPCB-TEQ05 are about 50 and 120 ng/g ww, respectively, which are lower than the corresponding iPCB6 limits of 125 and 300 ng/g ww set by the European Union.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".