A review of bioconcentration factor (BCF) and bioaccumulation factor (BAF) assessments for organic chemicals in aquatic organisms
Bibliographic record
Abstract
Bioaccumulation assessment is important in the scientific evaluation of risks that chemicals may pose to humans and the environment and is a current focus of regulatory effort. The status of bioaccumulation evaluations for organic chemicals in aquatic systems is reviewed to reduce uncertainty in bioaccumulation measurement, to provide quality data for assessment, and to assist in model development. A review of 392 scientific literature and database sources includes 5317 bioconcentration factor (BCF) and 1656 bioaccumulation factor (BAF) values measured for 842 organic chemicals in 219 aquatic species. A data quality assessment finds that 45% of BCF values are subject to at least one major source of uncertainty and that measurement errors generally result in an underestimation of actual BCF values. A case study of organic chemicals on the Canadian Domestic Substances List indicates that empirical data are available for less than 4% of the chemicals that require evaluation and of these chemicals, 76% have less than three acceptable quality BCF or BAF values. Field BAFs tend to be greater than laboratory BCFs emphasizing the importance of environmental measurement for reliable assessment; however, only 0.2% of current use organic chemicals have BAF measurements. Key parameters influencing uncertainty and variability in BCF and BAF data are discussed using reviewed data and models. A critical evaluation of representative BCF and BAF models in relation to existing measurements and regulatory criteria in Canada indicate the probability of Type II errors, i.e., false negatives or ``misses'', using BCF models for bioaccumulation assessment may be as high as 70.6% depending on the model. Recommendations for the selection of measured and modelled values used in bioaccumulation assessment are provided, and improvements for the science and regulatory criteria are proposed.Key words: bioconcentration, bioconcentration factor, bioaccumulation, bioaccumulation factor, octanol–water partition coefficient, fish.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".