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Record W3092816750 · doi:10.1073/pnas.1919139117

Global diversity of microbial communities in marine sediment

2020· article· en· W3092816750 on OpenAlexfundno aff
Tatsuhiko Hoshino, Hideyuki Doi, Go‐Ichiro Uramoto, Lars Wörmer, Rishi R. Adhikari, Nan Xiao, Yuki Morono, Steven D’Hondt, Kai‐Uwe Hinrichs, Fumio Inagaki

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Community Ecology and Physiology
Canadian institutionsnot available
FundersJapan Society for the Promotion of ScienceMinistry of Education, Culture, Sports, Science and TechnologyAlfred P. Sloan FoundationDeutsche ForschungsgemeinschaftCenter for Dark Energy Biosphere InvestigationsJapan Agency for Marine-Earth Science and TechnologyCanadian Institute for Advanced ResearchNational Science Foundation
KeywordsSpecies richnessSedimentEcologyBiodiversitySedimentary rockEnvironmental scienceTotal organic carbonArchaeaOceanographyBiomass (ecology)BiologyGeologyPaleontologyBacteria

Abstract

fetched live from OpenAlex

Significance Marine sediment covers 70% of Earth’s surface and harbors as much biomass as seawater. However, the global taxonomic diversity of marine sedimentary communities, and the spatial distribution of that diversity remain unclear. We investigated microbial composition from 40 globally distributed sampling locations, spanning sediment depths of 0.1 to 678 m. Statistical analysis reveals that oxygen presence or absence and organic carbon concentration are key environmental factors for defining taxonomic composition and diversity of marine sedimentary communities. Global marine sedimentary taxonomic richness predicted by species–area relationship models is 7.85 × 10 3 to 6.10 × 10 5 for Archaea and 3.28 × 10 4 to 2.46 × 10 6 for Bacteria as amplicon sequence variants, which is comparable to the richness in seawater and that in topsoil.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.042
GPT teacher head0.264
Teacher spread0.222 · 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

Citations415
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the National Academy of SciencesSame topicMicrobial Community Ecology and PhysiologyFrench-language works237,207