Relationship between the Bioconcentration Factor (BCF), the Bioaccumulation Factor (BAF), and the Trophic Magnification Factor (TMF)
Bibliographic record
Abstract
A degree of consensus exists among bioaccumulation scientists on the use of Trophic Magnification Factors (TMFs), which are calculated from patterns of observed tissue contaminant concentrations across a food web, as "conclusive" evidence of the bioaccumulative nature of chemicals in the environment.However, most regulatory criteria to determine whether a substance bioaccumulates rely on Bioconcentration Factors (BCFs), which are measured in single-species laboratory tests.BCFs do not account for chemical biomagnification via trophic transfer, nor do they reflect biodilution.I present the results from laboratory and field studies aimed at testing the hypothesis whether the BCF, or its field-based counterpart, the Bioaccumulation Factor (BAF) are adequate predictors of the TMF.I conclude that the BCF can be a useful predictor of the TMF for chemicals with certain characteristics (i.e., fat soluble substances), but that there are two major types of errors where the BCF does not provide accurate information about the bioaccumulative nature of chemicals in the environment.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".