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Record W4411709720 · doi:10.1038/s41467-025-61008-5

The microbiota vault initiative: safeguarding Earth’s microbial heritage for future generations

2025· review· en· W4411709720 on OpenAlexaff
Maria Gloria Domínguez-Bello, Dominik Steiger, Manuel Fankhauser, Adrian Egli, Pascale Vonaesch, Nicholas A. Bokulich, Anton Lavrinienko, Christian Hoffmann, Petra Zimmermann, Abdifatah Muktar Muhummed, Somphou Sayasone, Amma Larbi, Sègla Wilfrid Padonou, Jakob Zinsstag, Rea Tschopp, Chalat Santivarangkna, Diana Albertos Torres, Yuen Yi Li, Vanni Benvenga, Youzheng Teo, Marcel Houngbédji, Alex Kwarteng, Rob Knight, Jack A. Gilbert, Martin J. Blaser

Bibliographic record

VenueNature Communications · 2025
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsCanadian Institute for Advanced Research
FundersUniversität ZürichGebert Rüf StiftungEidgenössische Technische Hochschule ZürichSeerave Foundation
KeywordsSafeguardingVault (architecture)Earth (classical element)Environmental resource managementGeographyMedicineEnvironmental scienceArchaeology

Abstract

fetched live from OpenAlex

Microbial ecosystems are fundamental to planetary and human health, yet human activities are accelerating their loss. Disruptions to microbial communities undermine environmental stability, biodiversity, and health. Urgent action is required to preserve microbial diversity. The Microbiota Vault Initiative provides a global framework to safeguard microbiomes from human, animal, and environmental sources. It proactively archives microbial diversity for future needs, prioritizing depositor sovereignty, equitable collaboration, and ethical governance. By sharing limited information on deposits, the initiative fosters microbial conservation and collaboration between local and global researchers. It complements other efforts to ensure the resilience of microbiomes in an era of rapid environmental change.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0030.003
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 head0.032
GPT teacher head0.373
Teacher spread0.341 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations19
Published2025
Admission routes1
Has abstractyes

Explore more

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