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Record W2942260986 · doi:10.1038/s41467-019-09870-y

Acceleration of dolomitization by zinc in saline waters

2019· article· en· W2942260986 on OpenAlexfundno aff
Veerle Vandeginste, Oliver Snell, Matthew R. Hall, Elisabeth Steer, Arne Vandeginste

Bibliographic record

VenueNature Communications · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeochemistry and Elemental Analysis
Canadian institutionsnot available
FundersBritish Geological SurveyEngineering and Physical Sciences Research CouncilMcMaster University
KeywordsDolomiteDolomitizationZincMagnesiumGypsumChemistryCarbonateInorganic chemistryMineralogyGeologyPaleontology

Abstract

fetched live from OpenAlex

Abstract Dolomite (CaMg(CO 3 ) 2 ) plays a key role in the global carbon cycle. Yet, the chemical mechanisms that catalyze its formation remain an enigma. Here, using batch reactor experiments, we demonstrate an unexpected acceleration of dolomite formation by zinc in saline fluids, reflecting a not uncommon spatial association of dolomite with Mississippi Valley-type ores. The acceleration correlates with dissolved zinc concentration, irrespective of the zinc source tested (ZnCl 2 and ZnO). Moreover, the addition of dissolved zinc counteracts the inhibiting effect of dissolved sulfate on dolomite formation. Integration with previous studies enables us to develop an understanding of the dolomitization pathway. Our findings suggest that the fluids’ high ionic strength and zinc complexation facilitate magnesium ion dehydration, resulting in a dramatic decrease in induction time. This study establishes a previously unrecognized role of zinc in dolomite formation, and may help explain the changes in dolomite abundance through geological time.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.734

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.008
GPT teacher head0.235
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; a candidate call from one teacher head, 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

Citations52
Published2019
Admission routes1
Has abstractyes

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