Development and Evaluation of Aquatic and Terrestrial Food Web Bioaccumulation Models for Per- and Polyfluoroalkyl Substances
Bibliographic record
Abstract
There is a need for reliable models to predict the food web bioaccumulation and assess ecological and human health risks of per- and polyfluoroalkyl substances (PFAS). This present study presents (i) the development of novel mechanistic aquatic and terrestrial food web bioaccumulation models for PFAS and (ii) an evaluation of model performance using available laboratory and field data. Model predictions of laboratory-measured bioconcentration factors and field-based bioaccumulation factors of PFAS in fish were in good agreement with observed data as measured by the mean model bias (MB), representing systematic over- or under-estimation and the standard deviation of the MB, representing general uncertainty. The models provide a mechanistic framework for evaluating the combined effect of simultaneously occurring uptake and elimination processes and indicate food web-specific magnification of PFAS, with the highest degree of biomagnification occurring in food webs composed of air-breathing wildlife. Albumin-water, structural protein-water, membrane-water distribution coefficients, and renal clearance rate are among the most important model parameters. With further development and testing, these models may be useful for future PFAS screening and risk assessment initiatives and advance bioaccumulation studies of PFAS by providing a mechanistic framework for PFAS bioaccumulation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".