Modeling PCB-Bioaccumulation in the Bottlenose Dolphin (Tursiops truncatus): Estimating a Dietary Threshold Concentration
Bibliographic record
Abstract
An individually based (IB) model to predict PCB concentrations in the bottlenose dolphin population of Charleston, SC, USA, was developed with the aim to gain a better understanding of the bioaccumulation behavior and health risk of dietary PCBs across the population and their prey. PCB concentrations predicted in male and female bottlenose dolphin were in good agreement with observed tissue concentrations corroborating the reliability of the model performance and its utility in gaining a more complete view of risk. The modeled cumulative distribution of ΣPCB concentrations for the population with a breakdown into juvenile, adult male, and female subclasses ranged from 3600 to 144,400 ng/g lipid with 66% to >80% of the population exceeding the established threshold for adverse health effects of 17,000 ng/g lipid. The model estimated that a dietary PCB concentration not exceeding 5.1 ng/g wet wt would be required to reach a condition where 95% of the population would have tissue levels below the health effect threshold. The IB model for PCBs in bottlenose dolphins provides a novel approach to estimating the maximum acceptable dietary concentration for PCBs, a central and important factor to protect these apex predators. The model also enables effective prediction of concentrations in dolphins from fish contaminant surveys which are logistically easier and less costly to collect.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".