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Record W2332579020 · doi:10.1089/ast.2015.1365

Protection of Nitrate-Reducing Fe(II)-Oxidizing Bacteria from UV Radiation by Biogenic Fe(III) Minerals

2016· article· en· W2332579020 on OpenAlexafffund
Tina Gauger, Kurt O. Konhauser, Andreas Kappler

Bibliographic record

VenueAstrobiology · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeochemistry and Elemental Analysis
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaEberhard Karls Universität TübingenDeutsche Forschungsgemeinschaft
KeywordsMicroorganismEnvironmental chemistryChemistryNitrateOxidizing agentEarly EarthBacteriaAstrobiologyBiology

Abstract

fetched live from OpenAlex

Due to the lack of an ozone layer in the Archean, ultraviolet radiation (UVR) reached early Earth's surface almost unattenuated; as a consequence, a terrestrial biosphere in the form of biological soil crusts would have been highly susceptible to lethal doses of irradiation. However, a self-produced external screen in the form of nanoparticular Fe(III) minerals could have effectively protected those early microorganisms. In this study, we use viability studies by quantifying colony-forming units (CFUs), as well as Fe(II) oxidation and nitrate reduction rates, to show that encrustation in biogenic and abiogenic Fe(III) minerals can protect a common soil bacteria such as the nitrate-reducing Fe(II)-oxidizing microorganisms Acidovorax sp. strain BoFeN1 and strain 2AN from harmful UVC radiation. Analysis of DNA damage by quantifying cyclobutane pyrimidine dimers (CPD) confirmed the protecting effect by Fe(III) minerals. This study suggests that Fe(II)-oxidizing microorganisms, as would have grown in association with mafic and ultramafic soils/outcrops, would have been able to produce their own UV screen, enabling them to live in terrestrial habitats on early Earth.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.999

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)0.0040.000

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 head0.009
GPT teacher head0.187
Teacher spread0.178 · 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 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

Citations8
Published2016
Admission routes2
Has abstractyes

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