Occurrence of 80 per and polyfluorinated alkyl substances (PFAS) in muscle and liver tissues of marine mammals of the St. Lawrence Estuary and Gulf, Quebec, Canada
Bibliographic record
Abstract
Limited data are available for the occurrence of more recent per and polyfluorinated alkyl substances (PFAS) in marine mammals, especially from the St. Lawrence Estuary and Gulf. This study investigates the occurrence of PFAS in liver and muscle tissues of various marine mammals, including the harbor seal, gray seal, harp seal, hooded seal, harbor porpoise, white-sided dolphin, white-beaked dolphin, and True’s beaked whale. Among the 80 target PFAS (including PFCAs, PFSAs, Cyclic PFSA, FASAs/FASAAs, FTCAs/FTUCAs, FTSAs, Ether-PFAS, diPAPs, and ESI + ECF precursors) perfluorooctanoic sulfonate (PFOS) dominates in all the marine mammal species and several other long-chain PFCAs, such as PFNA, PFDA, PFUnA, PFTrDA, and PFHxDA, were detected at 100% frequency in both muscle and liver samples. PFDoA and 7:3 fluorotelomer carboxylic acid (7:3 acid) also showed a 100% detection frequency for liver samples. Harp seal tissues displayed notably low PFAS concentrations, with average total PFAS concentrations of 7 ng/g (ww: wet weight) in muscle and 44 ng/g (ww) in the liver. In contrast, the white-sided dolphin exhibited the highest average concentrations, reaching 39 ng/g (ww) in muscle and 334 ng/g (ww) in liver samples. The Pearson correlation analysis reveals a strong correlation between the concentration of PFOS, perfluoroalkyl carboxylic acids (PFCAs), and electrochemical fluorination (ECF) precursors. Species at the top of the marine food chain (harbor porpoise, white sided dolphin, and white beaked dolphin) presented the highest concentrations of PFAS, particularly PFOS and long-chain PFCAs, highlighting the need for an increased regulation of these persistent molecules in order to protect marine mammal’s health.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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 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".