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Record W3038067260 · doi:10.1038/s41598-020-67279-w

Clay minerals as a source of cadmium to estuaries

2020· article· en· W3038067260 on OpenAlexafffund
Weiduo Hao, Teruhiko Kashiwabara, Rong Jin, Yoshio Takahashi, Murray K. Gingras, Daniel S. Alessi, Kurt O. Konhauser

Bibliographic record

VenueScientific Reports · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsUniversity of Alberta
FundersJapan Agency for Marine-Earth Science and TechnologyJapan Society for the Promotion of ScienceNatural Sciences and Engineering Research Council of CanadaUniversity of TokyoChina Scholarship Council
KeywordsIlliteKaoliniteClay mineralsIonic strengthMontmorilloniteSeawaterCadmiumSorptionChemistryDesorptionAdsorptionEnvironmental chemistryTrace metalMetalInorganic chemistryMineralogyGeology

Abstract

fetched live from OpenAlex

Given the high surface reactivity of clay minerals, it is assumed that flocculation will lead to metal accumulation in marginal marine settings. However, the degree of metal sorption to clays is impacted by solution pH and ionic strength, and it remains unknown whether riverine clays indeed serve as a metal sink once they encounter seawater where pH and ionic strength markedly increase. Here, we conducted cadmium (Cd) adsorption experiments to three types of common clay minerals - kaolinite, illite and montmorillonite. We found that 20-30% of Cd from illite and montmorillonite surfaces were desorbed when transitioning from freshwater to seawater pH and ionic strength conditions, while kaolinite showed no discernible differences. Synchrotron X-ray adsorption spectroscopy confirmed that Cd release corresponded to a change in bonding from outer- to inner-sphere complexes when clays encountered seawater pH and ionic strength conditions. If other trace nutrients (such as Cu, Zn, Co) adsorbed onto riverine clay minerals behave in a similar manner to Cd, we speculate that their desorption in marginal marine settings should exert a significant impact on the productivity of the biosphere.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.552
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.002

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 head0.017
GPT teacher head0.243
Teacher spread0.226 · 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; both teacher heads agree on what is shown here.

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

Citations30
Published2020
Admission routes2
Has abstractyes

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