{"id":"W4233004170","doi":"10.7287/peerj.preprints.27295","title":"QIIME 2: Reproducible, interactive, scalable, and extensible microbiome data science","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":190,"is_retracted":false,"has_abstract":true,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia; Dalhousie University","funders":"","keywords":"Microbiome; Visualization; Scalability; Computer science; Data science; Metagenomics; Modular design; Data visualization; Computational biology; Biology; Bioinformatics; Data mining; Database","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["metaresearch","open_science","insufficient_payload"],"category_scores_codex":[0.03655167,0.0004120146,0.0006162478,0.001966399,0.0007218762,0.006960989,0.01226117,0.0001340202,0.001188282],"category_scores_gemma":[0.01552761,0.0003003547,0.00007350581,0.003280925,0.002282429,0.002069625,0.07905652,0.0004809699,0.00189448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009263994,"about_ca_system_score_gemma":0.0005887798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004672665,"about_ca_topic_score_gemma":0.00006695542,"domain_scores_codex":[0.98665,0.0002018223,0.001208231,0.008585377,0.002638926,0.0007155781],"domain_scores_gemma":[0.9753804,0.0008422047,0.0007064235,0.02110678,0.001623562,0.0003406282],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003303869,0.0001489983,0.001365656,0.00003502044,0.00004247236,0.00001627116,0.0002604781,0.00007418965,0.002231343,0.0005736778,0.9130768,0.08214206],"study_design_scores_gemma":[0.0006021318,0.000124749,0.02760676,0.0005371723,0.0001122324,0.000115395,0.001184762,0.157364,0.005110268,0.04885048,0.7567206,0.001671436],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.597304,0.001570168,0.06688393,0.009109687,0.04144058,0.002763364,0.001853931,0.001025835,0.2780485],"genre_scores_gemma":[0.7218956,0.000173883,0.1685639,0.0014901,0.001319726,0.00002039507,0.0005716952,0.00007058649,0.105894],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1721544,"threshold_uncertainty_score":0.9999449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3019415862077793,"score_gpt":0.4600509792033943,"score_spread":0.158109392995615,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}