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Record W4282574347 · doi:10.3390/w14121891

Chemical Oxidation and Reduction Pathways of Mercury Relevant to Natural Waters: A Review

2022· review· en· W4282574347 on OpenAlexaff
Lin Si, Brian A. Branfireun, Jessica Fierro

Bibliographic record

VenueWater · 2022
Typereview
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsWestern University
FundersAuburn University at MontgomeryAuburn University
KeywordsMercury (programming language)Environmental chemistryRedoxAbiotic componentChemistryNatural organic matterMethylmercuryCarbonateAquatic environmentPollutionOrganic matterInorganic chemistryBioaccumulationEcologyOrganic chemistry

Abstract

fetched live from OpenAlex

Mercury (Hg) pollution in the environment is a global issue and the toxicity of mercury depends on its speciation. Chemical redox reactions of mercury in an aquatic environment greatly impact on Hg evasion to the atmosphere and the methylation of mercury in natural waters. Identifying the abiotic redox pathways of mercury relevant to natural waters is important for predicting the transport and fate of Hg in the environment. The objective of this review is to summarize the current state of knowledge on specific redox reactions of mercury relevant to natural waters at a molecular level. The rate constants and factors affecting them, as well as the mechanistic information of these redox pathways, are discussed in detail. Increasing experimental evidence also implied that the structure of natural organic matter (NOM) play an important role in dark Hg(II) reduction, dark Hg(0) oxidation and Hg(II) photoreduction in the aquatic environment. Significant photooxidation pathways of Hg(0) identified are Hg(0) photooxidation by hydroxyl radical (OH•) and by carbonate radical (CO3−•). Future research needs on improving the understanding of Hg redox cycling in natural waters are also proposed.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.983
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0020.000

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 head0.045
GPT teacher head0.297
Teacher spread0.253 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations31
Published2022
Admission routes1
Has abstractyes

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