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Record W2576407813 · doi:10.1038/ismej.2016.175

Impacts of chemical gradients on microbial community structure

2017· article· en· W2576407813 on OpenAlexafffund
Jianwei Chen, Anna Hanke, Halina E. Tegetmeyer, Ines Kattelmann, Ritin Sharma, Emmo Hamann, Theresa Hargesheimer, Beate Kraft, Sabine Lenk, Jeanine S. Geelhoed, Robert L. Hettich, Marc Strous

Bibliographic record

VenueThe ISME Journal · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Community Ecology and Physiology
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaMax-Planck-Gesellschaft
KeywordsBiologyMicrobial population biologyMicrobial ecologyCommunity structureEcologyBacteriaGenetics

Abstract

fetched live from OpenAlex

Succession of redox processes is sometimes assumed to define a basic microbial community structure for ecosystems with oxygen gradients. In this paradigm, aerobic respiration, denitrification, fermentation and sulfate reduction proceed in a thermodynamically determined order, known as the 'redox tower'. Here, we investigated whether redox sorting of microbial processes explains microbial community structure at low-oxygen concentrations. We subjected a diverse microbial community sampled from a coastal marine sediment to 100 days of tidal cycling in a laboratory chemostat. Oxygen gradients (both in space and time) led to the assembly of a microbial community dominated by populations that each performed aerobic and anaerobic metabolism in parallel. This was shown by metagenomics, transcriptomics, proteomics and stable isotope incubations. Effective oxygen consumption combined with the formation of microaggregates sustained the activity of oxygen-sensitive anaerobic enzymes, leading to braiding of unsorted redox processes, within and between populations. Analyses of available metagenomic data sets indicated that the same ecological strategies might also be successful in some natural ecosystems.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
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 score0.780
Threshold uncertainty score1.000

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.261
Teacher spread0.243 · 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

Citations107
Published2017
Admission routes2
Has abstractyes

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