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Record W2906038629 · doi:10.5194/bg-15-7451-2018

Hydrothermal alteration of aragonitic biocarbonates: assessment of micro- and nanostructural dissolution–reprecipitation and constraints of diagenetic overprint from quantitative statistical grain-area analysis

2018· article· en· W2906038629 on OpenAlexaff
Laura A. Casella, Sixin He, Erika Griesshaber, Lourdes Fernández-Díaz, Martina Greiner, Elizabeth M. Harper, Daniel J. Jackson, Andreas Ziegler, Vasileios Mavromatis, Martin Dietzel, Anton Eisenhauer, S. Veintemillas‐Verdaguer, Uwe Brand, Wolfgang W. Schmahl

Bibliographic record

VenueBiogeosciences · 2018
Typearticle
Languageen
FieldMaterials Science
TopicCalcium Carbonate Crystallization and Inhibition
Canadian institutionsBrock University
FundersDeutsche Forschungsgemeinschaft
KeywordsDiagenesisCalciteAragoniteCarbonateDissolutionGeologyMineralogyMineralHydrothermal circulationGeochemistryChemistryMaterials sciencePaleontologyMetallurgy

Abstract

fetched live from OpenAlex

Abstract. The assessment of diagenetic overprint on microstructural and geochemical data gained from fossil archives is of fundamental importance for understanding palaeoenvironments. The correct reconstruction of past environmental dynamics is only possible when pristine skeletons are unequivocally distinguished from altered skeletal elements. Our previous studies show (i) that replacement of biogenic carbonate by inorganic calcite occurs via an interface-coupled dissolution–reprecipitation mechanism. (ii) A comprehensive understanding of alteration of the biogenic skeleton is only given when structural changes are assessed on both, the micrometre as well as on the nanometre scale.In the present contribution we investigate experimental hydrothermal alteration of six different modern biogenic carbonate materials to (i) assess their potential for withstanding diagenetic overprint and to (ii) find characteristics for the preservation of their microstructure in the fossil record. Experiments were performed at 175 °C with a 100 mM NaCl + 10 mM MgCl2 alteration solution and lasted for up to 35 days. For each type of microstructure we (i) examine the evolution of biogenic carbonate replacement by inorganic calcite, (ii) highlight different stages of inorganic carbonate formation, (iii) explore microstructural changes at different degrees of alteration, and (iv) perform a statistical evaluation of microstructural data to highlight changes in crystallite size between the pristine and the altered skeletons.We find that alteration from biogenic aragonite to inorganic calcite proceeds along pathways where the fluid enters the material. It is fastest in hard tissues with an existing primary porosity and a biopolymer fabric within the skeleton that consists of a network of fibrils. The slowest alteration kinetics occurs when biogenic nacreous aragonite is replaced by inorganic calcite, irrespective of the mode of assembly of nacre tablets. For all investigated biogenic carbonates we distinguish the following intermediate stages of alteration: (i) decomposition of biopolymers and the associated formation of secondary porosity, (ii) homoepitactic overgrowth with preservation of the original phase leading to amalgamation of neighbouring mineral units (i.e. recrystallization by grain growth eliminating grain boundaries), (iii) deletion of the original microstructure, however, at first, under retention of the original mineralogical phase, and (iv) replacement of both, the pristine microstructure and original phase with the newly formed abiogenic product.At the alteration front we find between newly formed calcite and reworked biogenic aragonite the formation of metastable Mg-rich carbonates with a calcite-type structure and compositions ranging from dolomitic to about 80 mol % magnesite. This high-Mg calcite seam shifts with the alteration front when the latter is displaced within the unaltered biogenic aragonite. For all investigated biocarbonate hard tissues we observe the destruction of the microstructure first, and, in a second step, the replacement of the original with the newly formed phase.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.521
Threshold uncertainty score0.891

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 studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.293
Teacher spread0.275 · 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 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

Citations34
Published2018
Admission routes1
Has abstractyes

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