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Record W4392238310 · doi:10.1111/sed.13185

Carbonate microbialites and chemotrophic microbes: Insights from caves from south‐east China

2024· article· en· W4392238310 on OpenAlexaff
Min Ren, Brian Jones, Xiaomin Nie, Xin Lin, Chuang Meng

Bibliographic record

VenueSedimentology · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPaleontology and Stratigraphy of Fossils
Canadian institutionsUniversity of Alberta
FundersFundamental Research Funds for the Central UniversitiesNational Natural Science Foundation of China
KeywordsCaveSpeleothemMicrobial matGeologyCalciteCarbonateChemosynthesisStalagmitePaleontologyGeochemistryEcologyCyanobacteriaBiologyHydrothermal ventChemistryBacteria

Abstract

fetched live from OpenAlex

ABSTRACT Chemosynthetic microorganisms facilitate microbialite development in many caves throughout the world. In Youqin Cave and Tian'e Cave, located in the Carboniferous–Triassic carbonates on the South China Block, five Quaternary speleothems (stalagmite, stalactite and cave pearl) that are 2.3 to 11.0 cm long were examined for their petrographic, geochemical and microbiological features to reveal how chemotrophs contribute to microbialite growth. In the speleothems, millimetre‐sized stromatolites, thrombolites and calcified microbial mats are characterized by alternating light, calcitic microlaminae and dark, clay and organic‐rich calcite microlaminae. Filamentous (reticulate, smooth, nodular and helical), coccoid and bacilliform microbes, originally carried into the caves from surface soils, are more common in the dark microlaminae/clots than in the light microlaminae. 16S rRNA gene sequencing shows that the biotas in the microbialites are dominated by chemoorganotrophic heterotrophic bacteria, including primarily Sphingomonas , Crossiella and Acinetobacter , and rare Archaea. Diverse metabolic pathways of these prokaryotes, including ureolysis, denitrification and nitrite ammonification, contributed to increases in localized pH and/or dissolved inorganic carbon in these microenvironments, prompting carbonate precipitation. Development of the cave microbialites was probably controlled by the evolution of the cave microbial community as environmental conditions changed. Microbialite growth was probably mediated by the microorganisms that flourished on the speleothem surfaces during periods of low drip water rates and slow calcite precipitation. The change from microstromatolites to microthrombolites was probably linked to a decrease in cell populations in the microbial communities. These cave microbialites provide clear insights regarding the biogenicity and growth mechanisms of chemosynthetic microbialites. Given their association with chemolithotrophic activities that can date back to the Meso‐Archean, cave microbialites provide insights into the biogenicity and growth mechanisms of chemosynthesis‐based microbialites throughout geological history.

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 score0.166
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
Bibliometrics0.0000.000
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 head0.007
GPT teacher head0.191
Teacher spread0.184 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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