Bibliographic record
Abstract
Partition coefficients that are used to predict concentrations of hydrophobic organic chemicals in biota (e.g., the bioconcentration factor) often assume that the sorptive capacity of an organism or tissue is adequately represented by its lipid content. In lean organisms and tissues, however, theory suggests that partitioning may be strongly influenced by the sorptive capacity of nonlipid materials, such as protein. Little is known about the sorptive capacity of proteins for hydrophobic organic chemicals, and methods to include proteins in bioaccumulation models do not exist. Here, we present a compilation and meta-analysis of published data to estimate the relative sorptive capacities of animal proteins and lipids for neutral organic chemicals. We found that the estimated sorptive capacity of protein in solid animal tissues ranged from around 1 to 10% that of lipid for compounds with a log octanol/water partition coefficient (K(OW)) of greater than two. The sorptive capacity of blood protein (albumin) appeared to be substantially higher than this, especially for low-K(OW) chemicals. For modeling purposes, we recommend estimating the sorptive capacity of animal protein as 5% that of lipid. According to this estimate, the sorptive capacity of an animal or tissue will be dominated by the contribution from protein if the lipid content makes up less than 5% of the dry-weight organic content. In such situations, a consideration of the sorptive capacity of nonlipid constituents, such as protein, will permit more accurate predictions of chemical accumulation and distribution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".