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Record W2570613845 · doi:10.1073/pnas.1611918114

Uranium(IV) adsorption by natural organic matter in anoxic sediments

2017· article· en· W2570613845 on OpenAlexafffund
Sharon Bone, James J. Dynes, John Cliff, John Bargar

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2017
Typearticle
Languageen
FieldChemistry
TopicRadioactive element chemistry and processing
Canadian institutionsCanadian Light Source (Canada)
FundersCanadian Institutes of Health ResearchU.S. Department of Energy
KeywordsAnoxic watersUraniumOrganic matterAdsorptionNatural (archaeology)Natural organic matterEnvironmental chemistryEnvironmental scienceGeochemistryGeologyEarth scienceChemistryPaleontologyMaterials scienceMetallurgyOrganic chemistry

Abstract

fetched live from OpenAlex

) in anoxic sediments; however, studies now show that this is not often the case. Yet a model of U(IV) speciation in the absence of mineral formation under field-relevant conditions has not yet been developed. Uranium(IV) speciation controls its reactivity, particularly its susceptibility to oxidative mobilization, impacting its distribution and toxicity. Here we show adsorption to organic carbon and organic carbon-coated clays dominate U(IV) speciation in an organic-rich natural substrate under field-relevant conditions. Whereas previous research assumed that U(IV) speciation is dictated by the mode of reduction (i.e., whether reduction is mediated by microbes or by inorganic reductants), our results demonstrate that mineral formation can be diminished in favor of adsorption, regardless of reduction pathway. Projections of U transport and bioavailability, and thus its threat to human and ecosystem health, must consider U(IV) adsorption to organic matter within the sediment environment.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.017

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)0.0010.000

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 head0.023
GPT teacher head0.298
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 source (direct Gemma or distilled Codex), 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

Citations198
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of the National Academy of SciencesSame topicRadioactive element chemistry and processingFrench-language works237,207