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Record W4307032661 · doi:10.1111/gbi.12527

A review of microbial‐environmental interactions recorded in Proterozoic carbonate‐hosted chert

2022· review· en· W4307032661 on OpenAlexaff
Kelsey Moore, Mirna Daye, Jian Gong, Kenneth H. Williford, Kurt O. Konhauser, Tanja Bosak

Bibliographic record

VenueGeobiology · 2022
Typereview
Languageen
FieldEarth and Planetary Sciences
TopicPaleontology and Stratigraphy of Fossils
Canadian institutionsUniversity of Alberta
FundersSimons Foundation
KeywordsProterozoicCarbonateGeologyMicrobial matDiagenesisLithologyGeologic recordSeawaterPaleontologyContext (archaeology)GeochemistryOceanographyChemistryCyanobacteriaTectonics

Abstract

fetched live from OpenAlex

The record of life during the Proterozoic is preserved by several different lithologies, but two in particular are linked both spatially and temporally: chert and carbonate. These lithologies capture a snapshot of dominantly peritidal environments during the Proterozoic. Early diagenetic chert preserves some of the most exceptional Proterozoic biosignatures in the form of microbial body fossils and mat textures. This fossiliferous and kerogenous chert formed in shallow marine environments, where chert nodules, layers, and lenses are often surrounded by and encased within carbonate deposits that themselves often contain kerogen and evidence of former microbial mats. Here, we review the record of biosignatures preserved in peritidal Proterozoic chert and chert-hosting carbonate and discuss this record in the context of experimental and environmental studies that have begun to shed light on the roles that microbes and organic compounds may have played in the formation of these deposits. Insights gained from these studies suggest temporal trends in microbial-environmental interactions and place new constraints on past environmental conditions, such as the concentration of silica in Proterozoic seawater, interactions among organic compounds and cations in seawater, and the influence of microbial physiology and biochemistry on selective preservation by silicification.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.000

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 head0.042
GPT teacher head0.282
Teacher spread0.240 · 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 designOther design
Domainnot available
GenreReview

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

Citations15
Published2022
Admission routes1
Has abstractyes

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