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Record W2963276645 · doi:10.1038/s41587-019-0209-9

Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2

2019· letter· en· W2963276645 on OpenAlexafffund
Evan Bolyen, Jai Ram Rideout, Matthew R. Dillon, Nicholas A. Bokulich, Christian C. Abnet, Gabriel A. Al‐Ghalith, Harriet Alexander, Eric J. Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E. Bisanz, Kyle Bittinger, Asker Brejnrod, Colin Brislawn, C. Titus Brown, Benjamin J. Callahan, Andrés Mauricio Caraballo‐Rodríguez, John Chase, Emily K. Cope, Ricardo Silva, Christian Diener, Pieter C. Dorrestein, Gavin M. Douglas, Daniel M. Durall, Claire Duvallet, Christian F. Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M. Gauglitz, Sean M. Gibbons, Deanna L. Gibson, Antonio González, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin Huttley, Stefan Janssen, Alan K. Jarmusch, Lingjing Jiang, Benjamin D. Kaehler, Kyo Bin Kang, Christopher R. Keefe, Paul Keim, Scott T. Kelley, Dan Knights, Irina Koester, Tomasz Kościółek, Jorden Kreps, Morgan G. I. Langille, Joslynn S. Lee, Ruth E. Ley, Yongxin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D Martin, Daniel McDonald, Lauren J. McIver, Alexey V. Melnik, Jessica L. Metcalf, Sydney Morgan, Jamie Morton, Ahmad Turan Naimey, José A. Navas-Molina, Louis‐Félix Nothias, Stephanie B. Orchanian, Talima Pearson, Samuel L Peoples, Daniel Petras, Mary L. Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam R. Rivers, Michael S. Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R. Spear, Austin D. Swafford, Luke Thompson, Pedro J. Torres, Anupriya Tripathi, Peter J. Turnbaugh, Sabah Ul-Hasan, Justin J. J. van der Hooft, Fernando Vargas, Yoshiki Vázquez‐Baeza, Emily Vogtmann, Max von Hippel, William A. Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C. Weber, Charles H. D. Williamson, Amy D. Willis, Zhenjiang Zech Xu, Jesse Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight, J. Gregory Caporaso

Bibliographic record

VenueNature Biotechnology · 2019
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaDalhousie University
FundersNational Institute of Environmental Health SciencesNational Institute of General Medical SciencesMedical Research CouncilUniversitetet i BergenAgricultural Research ServiceWashington Research FoundationHaukeland UniversitetssjukehusMedizinische Universität GrazSan Diego State UniversityNational Institutes of HealthNational Institute on Minority Health and Health DisparitiesKarl-Franzens-Universität GrazNational Cancer InstituteTianjin UniversityMax-Planck-GesellschaftChinese Academy of SciencesKarolinska InstitutetStockholms UniversitetNational Science FoundationUniversidad del AtlánticoFlorida Atlantic UniversityArizona Board of RegentsNatural Sciences and Engineering Research Council of CanadaU.S. Department of AgricultureAlfred P. Sloan FoundationMassachusetts Institute of TechnologyNational Health and Medical Research CouncilNorthern Arizona University
KeywordsMicrobiomeScalabilityExtensibilityComputer scienceComputational biologyData scienceBiologyBioinformaticsDatabaseProgramming language

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)0.011
metaresearch head score (Gemma)0.042
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: Not applicable
GenreCandidate signal: Software · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.042
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0040.004
Open science0.0030.004
Research integrity0.0050.011
Insufficient payload (model declined to judge)0.0070.007

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 head0.020
GPT teacher head0.313
Teacher spread0.293 · 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
GenreSoftware

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

Citations24,006
Published2019
Admission routes2
Has abstractno

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