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Record W3205339501 · doi:10.1016/j.isci.2021.103255

The footprint of dioxins in globally traded pork meat

2021· article· en· W3205339501 on OpenAlexaboutno aff
Kai‐Jie Chen, Tao Huang, Xiaodong Zhang, Xinrui Liu, Yufei Huang, Linfei Wang, Yuan Zhao, Hong Gao, Shu Tao, Junfeng Liu, Xiaohu Jian, Alexey Gusev, Jianmin Ma

Bibliographic record

VenueiScience · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsBioaccumulationContaminationEnvironmental scienceFood scienceChemistryBusinessEnvironmental chemistryBiologyEcology

Abstract

fetched live from OpenAlex

The bioaccumulation of polychlorinated dibenzo-p-dioxins/furans (PCDD/Fs), known as dioxins, in fatty meat is one of primary pathways of entry into the human body, but levels of human exposure to dioxins in fatty meat subject to global trade are unknown. We show high dioxin estimated dietary intake (EDI) via pork consumption in Europe, the United States, and China, owing to stronger dioxin environmental contamination and high pork consumption in these countries. The dioxin risk transfer embodied in pork trade is mostly significant in high-latitude countries and regions of Canada, Russia, and Greenland because these regions with low dioxin environmental levels import large amounts of pork meat from more severely dioxin-contaminated Europe and the United States. We demonstrate that global pig feed trading decreases the exposure of pork consumers to dioxins via the import of feed from countries with low dioxin environmental contamination by pig breeding countries.

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 score0.005
Threshold uncertainty score0.009

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 head0.012
GPT teacher head0.237
Teacher spread0.225 · 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

Citations9
Published2021
Admission routes1
Has abstractyes

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