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Record W4411497601 · doi:10.1038/s41564-025-02035-2

Guidelines for preventing and reporting contamination in low-biomass microbiome studies

2025· review· en· W4411497601 on OpenAlexaff
Noah Fierer, Pok Man Leung, Rachael Lappan, Raphael Eisenhofer, Francesco Ricci, Sophie I. Holland, Nicholas B. Dragone, Linda L. Blackall, Xiyang Dong, Cristina Dorador, Belinda C. Ferrari, Jacqueline Goordial, Susan Holmes, Fumio Inagaki, Tal Korem, Simone S. Li, Thulani P. Makhalanyane, Jessica L. Metcalf, Niranjan Nagarajan, William D. Orsi, Erin R. Shanahan, Alan W. Walker, Laura S. Weyrich, Jack A. Gilbert, Amy D. Willis, Benjamin J. Callahan, Ashley Shade, Julian Parkhill, Jillian F. Banfield, Chris Greening

Bibliographic record

VenueNature Microbiology · 2025
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsUniversity of Guelph
FundersDepartment of Microbiology, Faculty of Science, Chulalongkorn UniversityMonash Biomedicine Discovery Institute, Monash UniversitySchool of Life and Environmental Sciences, Deakin UniversityNational Health and Medical Research CouncilAustralian Research CouncilIrving Medical Center, Columbia UniversityHuck Institutes of the Life SciencesCentre for Biotechnology and BioengineeringVetAgro SupLudwig-Maximilians-Universität MünchenJapan Agency for Marine-Earth Science and TechnologyGenome Institute of SingaporeColorado State UniversityUniversiteit StellenboschUniversity of MelbourneMonash UniversityUniversity of SydneyUniversity of California, San DiegoUniversity of New South WalesCentre National de la Recherche ScientifiqueCooperative Institute for Research in Environmental SciencesUniversity of Colorado BoulderNational Science FoundationUniversity of AberdeenCollege of Veterinary Medicine, North Carolina State UniversityDepartment of Education and TrainingOffice of Polar ProgramsUniversity of WashingtonInstitut National de Recherche pour l'Agriculture, l'Alimentation et l'EnvironnementAgency for Science, Technology and ResearchHuman Frontier Science ProgramNorth Carolina State UniversityUniversidad de AntofagastaNational University of Singapore
KeywordsContaminationBiomass (ecology)MetagenomicsMicrobiomeEnvironmental scienceBiotechnologyEnvironmental chemistryBiologyEcologyBioinformaticsChemistry

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.051
metaresearch head score (Gemma)0.095
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 score0.051
Threshold uncertainty score0.269

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0510.095
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0070.004
Science and technology studies0.0010.005
Scholarly communication0.0040.003
Open science0.0070.004
Research integrity0.0090.007
Insufficient payload (model declined to judge)0.0060.007

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 head0.074
GPT teacher head0.458
Teacher spread0.384 · 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

Citations79
Published2025
Admission routes1
Has abstractno

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