Normalizing the Biomagnification Factor
Bibliographic record
Abstract
Abstract Following a recent proposal of normalizing the experimentally derived biomagnification factor (BMF) to a 5% lipid content in fish, we explore the normalization of the BMF of lipophilic chemicals in fish. We illustrate with theoretical models and experimental data that the BMF of lipophilic chemicals is a function of the lipid content of the diet and that poorly metabolizable, lipophilic chemicals biomagnify in organisms to a greater degree when present in higher–lipid content food. The proposed normalization of the laboratory BMF to the lipid content of the fish and subsequent standardization to a 5% fish lipid content, which is numerically identical to normalizing the BMF to a 5% dietary lipid content, has the potential to underestimate the biomagnification potential of lipophilic substances in aquatic food webs. The BMF normalized to both the lipid content of the fish and the lipid content of the diet, which is the biomagnification metric included in the Organisation for Economic Co-operation and Development's bioaccumulation testing guideline 305, better represents real-world biomagnification than the proposed BMF normalized and standardized to a 5% lipid content in fish. Environ Toxicol Chem 2021;40:1204–1211. © 2020 SETAC Abstract Normalizing the biomagnification factor. BMF = biomagnification factor; BMF5% = biomagnification factor normalized to the lipid content of the fish and standardized to a fish lipid content of 5%, BMFL = lipid-normalized BMF; BMFL,5% = BMF normalized to the lipid content of the fish and a lipid content of the diet of the fish of 0.05 kg lipid/kg diet; TMF = trophic magnification factor.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".