Processes influencing chemical biomagnification and trophic magnification factors in aquatic ecosystems: Implications for chemical hazard and risk assessment
Bibliographic record
Abstract
Bioconcentration factors (BCFs) and bioaccumulation factors (BAFs) are widely used in scientific and regulatory programs to assess chemical hazards. There is increasing interest in also using biomagnification factors (BMFs) and trophic magnification factors (TMFs) for this purpose, especially for highly hydrophobic substances that may reach high concentrations in predatory species that occupy high trophic level positions in ecosystems. Measurements of TMFs in specific ecosystems can provide invaluable confirmation that biomagnification or biodilution has occurred across food webs, but their use in a regulatory context can be controversial because of uncertainties related to the reliability of measurements and their regulatory interpretation. The objective of this study is to explore some of the recognized uncertainties and dependencies in field BMFs and TMFs. This is accomplished by compiling a set of three simple food web models (pelagic, demersal and combined pelagic-demersal) consisting of up to seven species to simulate field BMFs and TMFs and to explore their dependences on hydrophobicity (expressed as log KOW), rates of biotransformation and growth, sediment-water fugacity ratios, and extent of food web omnivory and issues that arise when chemical concentration gradients exist in aquatic ecosystems. It is shown that empirical TMFs can be highly sensitive to these factors, thus the use of TMFs in a regulatory context must recognize these sensitivities. It is suggested that simple but realistic evaluative food web models could be used to extend BCF and BAF assessments to include BMFs and TMFs, thus providing a tool to address bioaccumulation hazard and the potential risk of exposures to elevated chemical concentrations in organisms at high trophic levels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".