Use of the bioaccumulation factor to screen chemicals for bioaccumulation potential
Bibliographic record
Abstract
The fish bioconcentration factor (BCF), as calculated from controlled laboratory tests, is commonly used in chemical management programs to screen chemicals for bioaccumulation potential. The bioaccumulation factor (BAF), as calculated from field-caught fish, is more ecologically relevant because it accounts for dietary, respiratory, and dermal exposures. The BCFBAF™ program in the U.S. Environmental Protection Agency's Estimation Programs Interface Suite (EPI Suite™ Ver 4.10) screening-level tool includes the Arnot-Gobas quantitative structure-activity relationship model to estimate BAFs for organic chemicals in fish. Bioaccumulation factors can be greater than BCFs, suggesting that using the BAF rather than the BCF for screening bioaccumulation potential could have regulatory and resource implications for chemical assessment programs. To evaluate these potential implications, BCFBAF was used to calculate BAFs and BCFs for 6,034 U.S. high- and medium-production volume chemicals. The results indicate no change in the bioaccumulation rating for 86% of these chemicals, with 3% receiving lower and 11% receiving higher bioaccumulation ratings when using the BAF rather than the BCF. All chemicals that received higher bioaccumulation ratings had log K(OW ) values greater than 4.02, in which a chemical's BAF was more representative of field-based bioaccumulation than its BCF. Similar results were obtained for 374 new chemicals. Screening based on BAFs provides ecologically relevant results without a substantial increase in resources needed for assessments or the number of chemicals screened as being of concern for bioaccumulation potential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".