Methods for assessing the bioaccumulation of hydrocarbons and related substances in terrestrial organisms: A critical review
Bibliographic record
Abstract
Abstract This study investigates and reviews methods for the assessment of the terrestrial bioaccumulation potential of hydrocarbons and related organic substances. The study concludes that the unitless biomagnification factor (BMF) and/or the trophic magnification factor (TMF) are appropriate, practical, and thermodynamically meaningful metrics for identifying bioaccumulative substances in terrestrial food chains. The study shows that various methods, including physical–chemical properties like the KOA and KOW, in vitro biotransformation assays, quantitative structure–activity relationships, in vivo pharmacokinetic and dietary bioaccumulation tests, and field-based trophic magnification studies, can inform on whether a substance has the potential to biomagnify in a terrestrial food chain as defined by a unitless BMF exceeding 1. The study further illustrates how these methods can be arranged in a four-tier evaluation scheme for the purpose of screening assessments that aim to minimize effort and costs and expediate bioaccumulation assessment of the vast numbers of organic substances in commerce, identifies knowledge gaps, and provides recommendations for further research to improve bioaccumulation assessment. Integr Environ Assess Manag 2023;19:1433–1456. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".