Developing Methods for Assessing Trophic Magnification of Perfluoroalkyl Substances within an Urban Terrestrial Avian Food Web
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide We investigated the trophic magnification potential of perfluoroalkyl substances (PFAS) in a terrestrial food web by using a chemical activity-based approach, which involved normalizing concentrations of PFAS in biota to their relative biochemical composition in order to provide a thermodynamically accurate basis for comparing concentrations of PFAS in biota. Samples of hawk eggs, songbird tissues, and invertebrates were collected and analyzed for concentrations of 18 perfluoroalkyl acids (PFAAs) and for polar lipid, neutral lipid, total protein, albumin, and water content. Estimated mass fractions of PFCA C 8 –C 11 and PFSA C 4 –C 8 predominantly occurred in albumin within biota samples from the food web with smaller estimated fractions in polar lipids > structural proteins > neutral lipids and insignificant amounts in water. Estimated mass fractions of longer-chained PFAS (i.e., C 12 –C 16 ) mainly occurred in polar lipids with smaller estimated fractions in albumin > structural proteins > neutral lipids > and water. Chemical activity-based TMFs indicated that PFNA, PFDA, PFUdA, PFDoA, PFTrDA, PFTeDA, PFOS, and PFDS biomagnified in the food web; PFOA, PFHxDA, and PFHxS did not appear to biomagnify; and PFBS biodiluted. Chemical activity-based TMFs for PFCA C 8 –C 11 and PFSA C 4 –C 8 were in good agreement with corresponding TMFs derived with concentrations normalized to only total protein in biota, suggesting that concentrations normalized to total protein may be appropriate proxies of chemical activity-based TMFs for PFAS, which predominantly partition to albumin. Similarly, TMFs derived with concentrations normalized to albumin may be suitable proxies of chemical activity-based TMFs for longer-chained PFAS, which predominantly partition to polar lipids.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".