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Record W3092724194 · doi:10.1093/femsec/fiaa216

Islands in the sand: are all hypolithic microbial communities the same?

2020· article· en· W3092724194 on OpenAlexaff
Pedro H. Lebre, Eric Bottos, Thulani P. Makhalanyane, Ian D. Hogg, Don A. Cowan

Bibliographic record

VenueFEMS Microbiology Ecology · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicPolar Research and Ecology
Canadian institutionsThompson Rivers University
FundersAntarctica New ZealandNational Research Foundation
KeywordsBiological dispersalPhylogenetic treeEcologyHomogeneousPhylogenetic diversityBiologyMicrobial matMicrobial population biologyPaleontologyBacteria

Abstract

fetched live from OpenAlex

Hypolithic microbial communities (hypolithons) are complex assemblages of phototrophic and heterotrophic organisms associated with the ventral surfaces of translucent minerals embedded in soil surfaces. Past studies on the assembly, structure and function of hypolithic communities have tended to use composite samples (i.e. bulked hypolithic biomass) with the underlying assumption that samples collected from within a 'homogeneous' locality are phylogenetically homogeneous. In this study, we question this assumption by analysing the prokaryote phylogenetic diversity of multiple individual hypolithons: i.e. asking the seemingly simple question of 'Are all hypolithons the same'? Using 16S rRNA gene-based phylogenetic analysis of hypolithons recovered for a localized moraine region in the Taylor Valley, McMurdo Dry Valleys, Antarctica, we demonstrate that these communities are heterogeneous at very small spatial scales (<5 m). Using null models of phylogenetic turnover, we showed that this heterogeneity between hypolithons is probably due to stochastic effects such as dispersal limitations, which is entirely consistent with the physically isolated nature of the hypolithic communities ('islands in the sand') and the almost complete absence of a liquid continuum as a mode of microbial transport between communities.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.155
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.030
GPT teacher head0.245
Teacher spread0.215 · 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; both teacher heads agree on what is shown here.

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

Citations6
Published2020
Admission routes1
Has abstractyes

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