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Record W3040245690 · doi:10.1186/s40168-020-00875-0

Microbiome definition re-visited: old concepts and new challenges

2020· letter· en· W3040245690 on OpenAlexaff
Gabriele Berg, Daria Rybakova, Doreen Fischer, Tomislav Cernava, Marie-Christine Champomier Vergès, Trevor C. Charles, Xiaoyulong Chen, Luca Cocolin, Kellye Eversole, Gema Herrero Corral, Μαρία Κάζου, Linda L. Kinkel, Lene Lange, Nélson Lima, Alexander Loy, James Macklin, Emmanuelle Maguin, Tim H. Mauchline, Ryan McClure, Birgit Mitter, Matthew J. Ryan, Inga Sarand, Hauke Smidt, Bettina Schelkle, Hugo Roume, George Seghal Kiran, Joseph Selvin, Rafael Soares Correa de Souza, L.S. van Overbeek, Brajesh K. Singh, Michael Wagner, Aaron M. Walsh, Angela Sessitsch, Michael Schloter

Bibliographic record

VenueMicrobiome · 2020
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsAgriculture and Agri-Food CanadaBioinformatics Solutions (Canada)University of Waterloo
FundersHorizon 2020Agence Nationale de la RechercheEuropean Commission
KeywordsBiologyMicrobiomeMicrobial ecologyMedical microbiologyComputational biologyEvolutionary biologyEcologyBioinformaticsGeneticsImmunologyBacteria

Abstract

fetched live from OpenAlex

The field of microbiome research has evolved rapidly over the past few decades and has become a topic of great scientific and public interest. As a result of this rapid growth in interest covering different fields, we are lacking a clear commonly agreed definition of the term "microbiome." Moreover, a consensus on best practices in microbiome research is missing. Recently, a panel of international experts discussed the current gaps in the frame of the European-funded MicrobiomeSupport project. The meeting brought together about 40 leaders from diverse microbiome areas, while more than a hundred experts from all over the world took part in an online survey accompanying the workshop. This article excerpts the outcomes of the workshop and the corresponding online survey embedded in a short historical introduction and future outlook. We propose a definition of microbiome based on the compact, clear, and comprehensive description of the term provided by Whipps et al. in 1988, amended with a set of novel recommendations considering the latest technological developments and research findings. We clearly separate the terms microbiome and microbiota and provide a comprehensive discussion considering the composition of microbiota, the heterogeneity and dynamics of microbiomes in time and space, the stability and resilience of microbial networks, the definition of core microbiomes, and functionally relevant keystone species as well as co-evolutionary principles of microbe-host and inter-species interactions within the microbiome. These broad definitions together with the suggested unifying concepts will help to improve standardization of microbiome studies in the future, and could be the starting point for an integrated assessment of data resulting in a more rapid transfer of knowledge from basic science into practice. Furthermore, microbiome standards are important for solving new challenges associated with anthropogenic-driven changes in the field of planetary health, for which the understanding of microbiomes might play a key role. Video Abstract.

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)0.091
metaresearch head score (Gemma)0.128
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.091
Threshold uncertainty score0.481

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0910.128
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0120.010
Science and technology studies0.0080.051
Scholarly communication0.0200.071
Open science0.0100.021
Research integrity0.0120.036
Insufficient payload (model declined to judge)0.0050.003

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 head0.048
GPT teacher head0.274
Teacher spread0.225 · 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 designTheoretical or conceptual
Domainnot available
GenreCommentary

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

Citations2,160
Published2020
Admission routes1
Has abstractyes

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