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Record W4376871789 · doi:10.1021/acs.est.3c02676

Mercury and Sulfur Redox Cycling Affect Methylmercury Levels in Rice Paddy Soils across a Contamination Gradient

2023· article· en· W4376871789 on OpenAlexaff
Jiang Liu, Ji Chen, Alexandre J. Poulain, Qiang Pu, Zhengdong Hao, Bo Meng, Xinbin Feng

Bibliographic record

VenueEnvironmental Science & Technology · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsUniversity of Ottawa
FundersSouthwest UniversityNatural Science Foundation of Guizhou ProvinceNational Natural Science Foundation of China
KeywordsMethylmercuryEnvironmental chemistryMercury (programming language)ChemistrySoil waterSulfurContaminationDemethylationPaddy fieldCyclingRedoxEnvironmental scienceEcologyBioaccumulationDNA methylationInorganic chemistrySoil scienceBiologyBiochemistry

Abstract

fetched live from OpenAlex

Methylmercury (MeHg) contamination in rice via paddy soils is an emerging global environmental issue. An understanding of mercury (Hg) transformation processes in paddy soils is urgently needed in order to control Hg contamination of human food and related health impacts. Sulfur (S)-regulated Hg transformation is one important process that controls Hg cycling in agricultural fields. In this study, Hg transformation processes, such as methylation, demethylation, oxidation, and reduction, and their responses to S input (sulfate and thiosulfate) in paddy soils with a Hg contamination gradient were elucidated simultaneously using a multi-compound-specific isotope labeling technique ( 200 Hg II, Me 198 Hg, and 202 Hg 0 ). In addition to Hg II methylation and MeHg demethylation, this study revealed that microbially mediated reduction of Hg II, methylation of Hg 0, and oxidative demethylation–reduction of MeHg occurred under dark conditions; these processes served to transform Hg between different species (Hg 0, Hg II, and MeHg) in flooded paddy soils. Rapid redox recycling of Hg species contributed to Hg speciation resetting, which promoted the transformation between Hg 0 and MeHg by generating bioavailable Hg II for fuel methylation. Sulfur input also likely affected the microbial community structure and functional profile of Hg II methylators and, therefore, influenced Hg II methylation. The findings of this study contribute to our understanding of Hg transformation processes in paddy soils and provide much-needed knowledge for assessing Hg risks in hydrological fluctuation-regulated ecosystems.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.677
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
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 head0.018
GPT teacher head0.293
Teacher spread0.274 · 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

Citations59
Published2023
Admission routes1
Has abstractyes

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