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Record W2259939606 · doi:10.1038/srep15525

Microbial mediation of complex subterranean mineral structures

2015· article· en· W2259939606 on OpenAlexaff
Nicola Tisato, Stefano F. F. Torriani, Sylvain Monteux, Francesco Sauro, Jo De Waele, Maria Luisa Tavagna, Ilenia M. D’Angeli, Daniel Chailloux, Michel Renda, Timothy I. Eglinton, Tomaso R. R. Bontognali

Bibliographic record

VenueScientific Reports · 2015
Typearticle
Languageen
FieldDentistry
TopicOral and Maxillofacial Pathology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMineralMediationGeologyComputational biologyComputer scienceBiologyEcology

Abstract

fetched live from OpenAlex

Helictites--an enigmatic type of mineral structure occurring in some caves--differ from classical speleothems as they develop with orientations that defy gravity. While theories for helictite formation have been forwarded, their genesis remains equivocal. Here, we show that a remarkable suite of helictites occurring in Asperge Cave (France) are formed by biologically-mediated processes, rather than abiotic processes as had hitherto been proposed. Morphological and petro-physical properties are inconsistent with mineral precipitation under purely physico-chemical control. Instead, microanalysis and molecular-biological investigation reveals the presence of a prokaryotic biofilm intimately associated with the mineral structures. We propose that microbially-influenced mineralization proceeds within a gliding biofilm which serves as a nucleation site for CaCO3, and where chemotaxis influences the trajectory of mineral growth, determining the macroscopic morphology of the speleothems. The influence of biofilms may explain the occurrence of similar speleothems in other caves worldwide, and sheds light on novel biomineralization processes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
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 head0.060
GPT teacher head0.294
Teacher spread0.234 · 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 source (direct Gemma or distilled Codex), 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

Citations53
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueScientific ReportsSame topicOral and Maxillofacial PathologyFrench-language works237,207