A Bioenergetic Biomagnification Model for the Animal Kingdom
Bibliographic record
Abstract
Species vary greatly in the degree to which they accumulate dietary contaminants. Bioenergetic processes play a key role in chemical uptake and elimination, and interspecific variation in bioaccumulation can be attributed in large part to variation in how species feed, digest, and allocate energy. We present a quantitative treatment of this relationship for the entire animal kingdom. We derive a model to predict the biomagnification factor for nonmetabolizable, slowly eliminated chemicals, BMF(max). We test the model with observed biomagnification factors and independently derived bioenergetic parameters for a diverse suite of species, including herbivores and carnivores, heterotherms and homeotherms, vertebrates and invertebrates, adults and juveniles, domestic/laboratory animals and wild individuals from freshwater, marine, and terrestrial environments. The model successfully predicts species-specific BMF(max) values across this range of taxa, with values ranging from less than 1 in caterpillars to nearly 100 in some carnivores. In addition, we make novel predictions of BMF(max) for several taxa for which no measured bioaccumulation data are available. Our analysis provides new insights into the role of ecology in chemical dynamics across the animal kingdom, providing a general framework for understanding how characteristics of an organism and its ecological context influence the degree to which that organism accumulates chemicals present in its diet.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".