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Record W4206066912 · doi:10.1016/j.ecoenv.2021.113143

The relative importance of mercury methylation and demethylation in rice paddy soil varies depending on the presence of rice plants

2022· article· en· W4206066912 on OpenAlexaff
Rachel Strickman, Steven L. Larson, Haiyong Huang, Evangelos Kakouros, Mark Marvin-DiPasquale, Carl P. J. Mitchell, Rebecca B. Neumann

Bibliographic record

VenueEcotoxicology and Environmental Safety · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsUniversity of TorontoThe Scarborough Hospital
FundersNatural Resources, Energy and Science Authority of Sri LankaNational Science Foundation
KeywordsMethylmercuryDemethylationChemistryMercury (programming language)Environmental chemistrySoil waterMethylationRice plantPaddy fieldPoaceaeAgronomyOrganic matterDNA methylationSoil scienceBioaccumulationBiologyEnvironmental scienceBiochemistry

Abstract

fetched live from OpenAlex

Neurotoxic methylmercury (MeHg) accumulates in rice grain from paddy soil, where its concentration is controlled by microbial mercury methylation and demethylation. Both up- and down-regulation of methylation is known to occur in the presence of rice plants in comparison to non-vegetated paddy soils; the influence of rice plant presence/absence on demethylation is unknown. To assess the concurrent influence of rice plant presence/absence on methylation and demethylation, and to determine which process was more dominant in controlling soil MeHg concentrations, we maintained six rhizoboxes of paddy soil with and without rice plants. At the peak of plant growth, we simultaneously measured ambient MeHg, ambient inorganic mercury (IHg), and potential rate constants of methylation and demethylation (Kmeth and Kdemeth) in soil using stable isotope tracers and ID-GC-ICPMS. We also measured organic matter content, elemental S, and water-extractable sulfate. We found MeHg concentrations were differentially controlled by MeHg production and degradation processes, depending on whether plants were present. In non-vegetated boxes, MeHg concentration was controlled by Kmeth, as evidenced by a strong and positive correlation, while Kdemeth had no relation to MeHg concentration. These results indicate methylation was the dominant driver of MeHg concentration in non-vegetated soil. In vegetated boxes, Kdemeth strongly and negatively predicted MeHg concentration, indicating that demethylation was the dominant control in soil with plants. MeHg concentration, Kmeth, and % MeHg all had significantly less variance in vegetated than in non-vegetated soils due to a consistent elimination of greater values. This pattern suggests that reduced MeHg production capacity was a secondary control on MeHg concentrations in vegetated soils. We observed no difference in the magnitude or variance of Kdemeth between treatments, suggesting that demethylation was robust to soil chemical conditions influenced by the plant, perhaps because of a wider taxonomic diversity of demethylators. Our results suggest that methylation and demethylation processes could both be leveraged to alter MeHg concentrations in rice paddy soil.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.014
GPT teacher head0.238
Teacher spread0.223 · 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

Citations12
Published2022
Admission routes1
Has abstractyes

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