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Record W3093778043 · doi:10.1021/acs.est.0c00268

Methanogenesis Is an Important Process in Controlling MeHg Concentration in Rice Paddy Soils Affected by Mining Activities

2020· article· en· W3093778043 on OpenAlexaff
Qingqing Wu, Haiyan Hu, Bo Meng, Baolin Wang, Alexandre J. Poulain, Hua Zhang, Jinling Liu, Andrea G. Bravo, Kevin Bishop, Stefan Bertilsson, Xinbin Feng

Bibliographic record

VenueEnvironmental Science & Technology · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsUniversity of Ottawa
FundersState Key Laboratory of Environmental GeochemistryChinese Academy of SciencesNational Natural Science Foundation of China
KeywordsMethanogenesisMethylmercuryEnvironmental chemistryPaddy fieldSoil waterChemistryCyclingRhizosphereContaminationIncubationTailingsEnvironmental scienceAgronomyEcologySoil scienceBioaccumulationMethaneBiologyBacteria

Abstract

fetched live from OpenAlex

Rice paddies are agricultural sites of special concern because the potent toxin methylmercury (MeHg), produced in rice paddy soils, accumulates in rice grains. MeHg cycling is mostly controlled by microbes but their importance in MeHg production and degradation in paddy soils and across a Hg concentration gradient remains unclear. Here we used surface and rhizosphere soil samples in a series of incubation experiments in combination with stable isotope tracers to investigate the relative importance of different microbial groups on MeHg production and degradation across a Hg contamination gradient. We showed that sulfate reduction was the main driver of MeHg formation and concentration at control sites, and that methanogenesis had an important and complex role in MeHg cycling as Hg concentrations increased. The inhibition of methanogenesis at the mining sites led to an increase in MeHg production up to 16.6-fold and a decrease in MeHg degradation by up to 77%, suggesting that methanogenesis is associated with MeHg degradation as Hg concentrations increased. This study broadens our understanding of the roles of microbes in MeHg cycling and highlights methanogenesis as a key control of MeHg concentrations in rice paddies, offering the potential for mitigation of Hg contamination and for the safe production of rice in Hg-contaminated areas.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.176
Threshold uncertainty score0.832

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.013
GPT teacher head0.256
Teacher spread0.243 · 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 designBench or experimental
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

Citations82
Published2020
Admission routes1
Has abstractyes

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