Use of trophic magnification factors and related measures to characterize bioaccumulation potential of chemicals
Bibliographic record
Abstract
Recent technical workgroups have concluded that trophic magnification factors (TMFs) are useful in characterizing the bioaccumulation potential of a chemical, because TMFs provide a holistic measure of biomagnification in food webs. The objectives of this article are to provide a critical analysis of the application of TMFs for regulatory screening for bioaccumulation potential, and to discuss alternative methods for supplementing TMFs and assessing biomagnification in cases where insufficient data are available to determine TMFs. The general scientific consensus is that chemicals are considered bioaccumulative if they exhibit a TMF > 1. However, comparison of study-derived TMF estimates to this threshold value should be based on statistical analyses such that variability is quantified and false positive and false negative errors in classification of bioaccumulation potential are minimized. An example regulatory decision-making framework is presented to illustrate the use of statistical power analyses to minimize assessment errors. Suggestions for considering TMF study designs and TMFs obtained from multiple studies are also provided. Alternative bioaccumulation metrics are reviewed for augmenting TMFs and for substituting in situations in which field data for deriving TMFs are unavailable. Field-derived, trophic level-normalized biomagnification factors (BMF(TL) s), biota-sediment accumulation factors (BSAF(TL) s), and bioaccumulation factors (BAF(TL) s) are recommended if data are available, because these measures are most closely related to the biomagnification processes characterized by TMFs. Field- and laboratory-derived BAFs and bioconcentration factors are generally less accurate in predicting biomagnification. However, bioconcentration factors and BAFs remain useful for characterizing bioaccumulation as a result of the transfer of chemicals from abiotic environmental compartments to lower trophic levels. Modeling that incorporates available laboratory and field data should also be considered for augmenting assessments of bioaccumulation potential. Modeling can provide a TMF-focused assessment for new or unreleased chemicals in the absence of field data by estimating TMF values and theoretical relationships between physical-chemical properties and TMF values (quantitative structure-activity relationships). An illustration of the use of physicochemical properties for estimating TMFs is provided. Overall, TMFs provide valuable information regarding bioaccumulation potential and should be incorporated into regulatory decision making following the suggestions outlined in this article.
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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 | 0.017 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".