Molecular size cutoff criteria for screening bioaccumulation potential: Fact or fiction?
Bibliographic record
Abstract
It has been asserted that, when screening chemicals for bioaccumulation potential, molecular size cutoff criteria (or indicators) can be applied above which no, or limited, bioaccumulation is expected. The suggested molecular size values have increased over time as more measurements have become available. Most of the proposed criteria have been derived from unevaluated fish bioconcentration factor (BCF) data, and less than 5% of existing organic substances have measured BCFs.We critically review the proposed criteria, first by considering other factors that may also contribute to reduced bioaccumulation for larger molecules, namely, reduced bioavailability in the water column, reduced rate of uptake corresponding to reduced diffusion rates, and the effects of biotransformation and growth dilution. An evaluated BCF and bioaccumulation factor (BAF) database for more than 700 substances and dietary uptake efficiency data are compared against proposed cutoff values. We examine errors associated with interpreting BCF data, particularly for developing molecular size criteria of bioaccumulation potential. Reduced bioaccumulation that is often associated with larger molecular size can be explained by factors other than molecular size, and there is evidence of absorption of molecules exceeding the proposed cutoff criteria. The available data do not support strict cutoff criteria, indicating that the proposed values are incorrect. Rather than assessing bioaccumulation using specific chemical properties in isolation, holistic methods that account for competing rates of uptake and elimination in an organism are recommended. An integrated testing strategy is suggested to improve knowledge of the absorption and bioaccumulation of large substances.
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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 | 0.029 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".