MétaCan
Menu
Back to cohort
Record W2953684647 · doi:10.1016/j.envint.2019.104971

Corn (Zea mays L.): A low methylmercury staple cereal source and an important biospheric sink of atmospheric mercury, and health risk assessment

2019· article· en· W2953684647 on OpenAlexaff
Guangyi Sun, Xinbin Feng, Runsheng Yin, Huifang Zhao, Leiming Zhang, Jonas Sommar, Zhonggen Li, Hua Zhang

Bibliographic record

VenueEnvironment International · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsEnvironment and Climate Change Canada
FundersK. C. Wong Education FoundationChina Postdoctoral Science FoundationChinese Academy of SciencesNational Natural Science Foundation of China
KeywordsMercury (programming language)MethylmercuryEnvironmental scienceAgronomySink (geography)ContaminationEnvironmental chemistryStaple foodChemistryAgricultureBioaccumulationBiologyEcologyGeography

Abstract

fetched live from OpenAlex

In mercury (Hg) contaminated areas of Asia, human exposure to toxic methyl-Hg (MeHg) through a rice-based diet of locally produced crop may pose a health threat. Alternative cropping system to rice in such areas would be most desirable. In this study, corn, the leading cereal source in the world with large biomass, was demonstrated to accumulate an insignificant amount of MeHg from the soil in its edible portion compared to that in rice, suggesting corn being a very competitive alternative crop. By examining Hg stable isotope composition, Hg in the aerial parts of corn was found to be mostly from the atmosphere. Maize cropping worldwide is estimated to be a discemible sink of atmospheric Hg with approximately 44 Mg Hg accumulated in each growing season on a yearly basis, most of which is from foliar uptake of atmospheric Hg and this amount is comparable to litterfall Hg observed in North America and Europe. It is thus recommended to use corn as a replacement of rice in highly Hg-contaminated areas for remediation of Hg pollution in the food supply.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.011
GPT teacher head0.276
Teacher spread0.265 · 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.

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

Citations34
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueEnvironment InternationalSame topicMercury impact and mitigation studiesFrench-language works237,207