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Record W4417474398 · doi:10.1126/science.adr0351

Risks of per- and polyfluoroalkyl substance exposure through marine fish consumption

2025· article· en· W4417474398 on OpenAlexaff
Wenhui Qiu, Ge Yang, Ling Cao, Shan Niu, Yuzhe Li, Di Fang, Zhaomin Dong, Jason T. Magnuson, Daniel Schlenk, Kmy Leung, Yi Zheng, Zhenzhong Zeng, Lian Feng, Xianming Zhang, Yanxu Zhang, Wenhong Fan, Tao Huang, Jianmin Ma, Minghong Wu, Shu Tao, Chunmiao Zheng

Bibliographic record

VenueScience · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicPer- and polyfluoroalkyl substances research
Canadian institutionsConcordia University
FundersShenzhen Municipal Science and Technology Innovation CouncilScience, Technology and Innovation Commission of Shenzhen MunicipalityNational Natural Science Foundation of China
KeywordsPerfluorooctaneFish <Actinopterygii>HazardConsumption (sociology)Marine fishFood safety

Abstract

fetched live from OpenAlex

Global food trade expansion has enriched diets worldwide but also heightened concerns about contaminant spread. Per- and polyfluoroalkyl substances (PFAS) can persist in the environment for decades, yet their risks through food trade remain unclear. The global median estimated daily intake (EDI) of C8-PFAS (perfluorooctanoic acid and perfluorooctane sulfonate) (0.023 nanograms per kilograms per day) was mapped from 212 marine fish species, which indicated higher EDIs in North America, Oceania, and Europe. Furthermore, European countries play a pivotal role in C8-PFAS trade flows, markedly reshaping exposure pathways and driving increased exposure in many nations. These dynamics highlight the importance of establishing food-safety regulations and international trade standards. Although perfluorooctane sulfonate hazard index decreased by 72% after its 2009 regulations, unregulated long-chain PFAS continue to pose elevated risks.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.048
GPT teacher head0.346
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations23
Published2025
Admission routes1
Has abstractyes

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