A weight of evidence approach for bioaccumulation assessment
Bibliographic record
Abstract
Bioaccumulation assessments conducted by regulatory agencies worldwide use a variety of methods, types of data, metrics, and categorization criteria. Lines of evidence (LoE) for bioaccumulation assessment can include bioaccumulation metrics such as in vivo bioconcentration factor (BCF) and biomagnification factor (BMF) data measured from standardized laboratory experiments, and field (monitoring) data such as BMFs, bioaccumulation factors (BAFs), and trophic magnification factors (TMFs). In silico predictions from mass-balance models and quantitative structure-activity relationships (QSARs) and a combination of in vitro biotransformation rates and in vitro-in vivo extrapolation (IVIVE) models can also be used. The myriad bioaccumulation metrics and categorization criteria and underlying uncertainty in measured or modeled data can make decision-making challenging. A weight of evidence (WoE) approach is recommended to address uncertainty. The Bioaccumulation Assessment Tool (BAT) guides a user through the process of collecting and generating various LoE required for assessing the bioaccumulation of neutral and ionizable organic chemicals in aquatic (water-respiring) and air-breathing organisms. The BAT includes data evaluation templates (DETs) to critically evaluate the reliability of the LoE used in the assessment. The DETs were developed from standardized testing guidance. The approach used in the BAT is consistent with OECD and SETAC WoE principles and facilitates the implementation of chemical policy objectives in chemical assessment and management. The recommended methods are also iterative and tiered, providing pragmatic methods to reduce unnecessary animal testing. General concepts of the BAT are presented and case study applications of the tool for hexachlorobenzene (HCB) and β-hexachlorocyclohexane (β-HCH) are demonstrated. The BAT provides a consistent and transparent WoE framework to address uncertainty in bioaccumulation assessment and is envisaged to evolve with scientific and regulatory developments. Integr Environ Assess Manag 2023;19:1235-1253. © 2022 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 | 0.166 | 0.349 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.054 | 0.019 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".