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Record W2475077967 · doi:10.1021/acs.est.5b00195

Human Body Burden and Dietary Methylmercury Intake: The Relationship in a Rice-Consuming Population

2015· article· en· W2475077967 on OpenAlexaff
Ping Li, Xinbin Feng, Hing Man Chan, Xiaofeng Zhang, Buyun Du

Bibliographic record

VenueEnvironmental Science & Technology · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsOntario GenomicsUniversity of Ottawa
FundersMinistry of Science and Technology of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsMethylmercuryMercury (programming language)PopulationEnvironmental chemistryFish consumptionReference doseChemistryToxicologyAnimal scienceFish <Actinopterygii>BioaccumulationEnvironmental healthBiologyRisk assessmentFisheryMedicine

Abstract

fetched live from OpenAlex

Rice can be the main route of methylmercury (MeHg) exposure for rice-consuming populations living in area where mercury (Hg) is mined. However, the current risk assessment paradigm for MeHg exposure is based on epidemiological data collected from fish-consuming populations. This study was designed to evaluate the relationship between dietary MeHg intake and human body burden in a rice -consuming population from the Wanshan Hg mining area in China. Hair MeHg concentrations averaged 2.07 ± 1.79 μg/g, and the average blood MeHg concentration across the study area ranged from 2.20 to 9.36 μg/L. MeHg constituted 52.8 ± 17.5% and 71.7 ± 18.2% of total Hg (THg) on average in blood and hair samples, respectively. Blood and hair MeHg concentrations, rather than THg, can be used as a proxy of human MeHg exposure. Hair MeHg levels showed no significant monthly variation; however, hair THg can be impacted by inorganic Hg exposure. The toxicokinetic model of MeHg exposure based on fish consumption underestimated the human hair MeHg levels, and this may be a consequence of the high hair-to-blood MeHg ratio (361 ± 105) in the studied rice-consuming population. The use of risk assessment models based on fish consumption may not be appropriate for inland mining areas where rice is the staple food.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.055
Threshold uncertainty score0.938

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.036
GPT teacher head0.292
Teacher spread0.256 · 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 teacher head, 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

Citations81
Published2015
Admission routes1
Has abstractyes

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