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Record W2984918288 · doi:10.5194/bg-17-1955-2020

Vivianite formation in ferruginous sediments from Lake Towuti, Indonesia

2020· article· en· W2984918288 on OpenAlexafffund
Aurèle Vuillemin, André Friese, Richard Wirth, Jan A. Schuessler, Anja M. Schleicher, Helga Kemnitz, Andreas Lücke, Kohen W. Bauer, Sulung Nomosatryo, Friedhelm von Blanckenburg, Rachel L. Simister, Luis Ordóñez, Daniel Arizteguí, Cynthia Henny, James M. Russell, Satria Bijaksana, Hendrik Vogel, Sean A. Crowe, Jens Kallmeyer

Bibliographic record

VenueBiogeosciences · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeochemistry and Elemental Analysis
Canadian institutionsUniversity of British Columbia
FundersLeibniz-GemeinschaftNatural Sciences and Engineering Research Council of CanadaUniversitas Sam RatulangiInstitut Teknologi BandungUniversitas HasanuddinLembaga Ilmu Pengetahuan IndonesiaUniversity of BernUniversität zu KölnUniversité de GenèveUniversity of Minnesota DuluthUniversity of WindsorSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungUniversity of MinnesotaDeutsche ForschungsgemeinschaftRoyal Holloway, University of LondonGenome British ColumbiaBrown UniversityHelmholtz-Zentrum Potsdam - Deutsches GeoForschungsZentrum GFZNational Science Foundation
KeywordsDiagenesisGeologySideriteGeochemistrySedimentBiogeochemical cyclePore water pressurePyriteEnvironmental chemistryChemistryGeomorphology

Abstract

fetched live from OpenAlex

Abstract. Ferruginous lacustrine systems, such as Lake Towuti, Indonesia, are characterized by a specific type of phosphorus cycling in which hydrous ferric iron (oxyhydr)oxides trap and precipitate phosphorus to the sediment, which reduces its bioavailability in the water column and thereby restricts primary production. The oceans were also ferruginous during the Archean, thus understanding the dynamics of phosphorus in modern-day ferruginous analogues may shed light on the marine biogeochemical cycling that dominated much of Earth's history. Here we report the presence of large crystals (>5 mm) and nodules (>5 cm) of vivianite – a ferrous iron phosphate – in sediment cores from Lake Towuti and address the processes of vivianite formation, phosphorus retention by iron and the related mineral transformations during early diagenesis in ferruginous sediments. Core scan imaging, together with analyses of bulk sediment and pore water geochemistry, document a 30 m long interval consisting of sideritic and non-sideritic clayey beds and diatomaceous oozes containing vivianites. High-resolution imaging of vivianite revealed continuous growth of crystals from tabular to rosette habits that eventually form large (up to 7 cm) vivianite nodules in the sediment. Mineral inclusions like millerite and siderite reflect diagenetic mineral formation antecedent to the one of vivianite that is related to microbial reduction of iron and sulfate. Together with the pore water profiles, these data suggest that the precipitation of millerite, siderite and vivianite in soft ferruginous sediments stems from the progressive consumption of dissolved terminal electron acceptors and the typical evolution of pore water geochemistry during diagenesis. Based on solute concentrations and modeled mineral saturation indices, we inferred vivianite formation to initiate around 20 m depth in the sediment. Negative δ56Fe values of vivianite indicated incorporation of kinetically fractionated light Fe2+ into the crystals, likely derived from active reduction and dissolution of ferric oxides and transient ferrous phases during early diagenesis. The size and growth history of the nodules indicate that, after formation, continued growth of vivianite crystals constitutes a sink for P during burial, resulting in long-term P sequestration in ferruginous sediment.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.019
GPT teacher head0.200
Teacher spread0.181 · 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 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

Citations54
Published2020
Admission routes2
Has abstractyes

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