{"id":"W3198254902","doi":"10.1101/2021.09.02.458641","title":"Public human microbiome data dominated by highly developed countries","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institutes of Health","keywords":"Microbiome; Human microbiome; Population; Human Microbiome Project; Geography; Genomics; Data science; Biology; Environmental health; Bioinformatics; Genome; Computer science; Genetics; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001600365,0.0004590485,0.0006747157,0.003352749,0.0007591513,0.002391026,0.0004355342,0.0003365704,0.01200654],"category_scores_gemma":[0.00714764,0.0001877747,0.000434898,0.01466713,0.0004777473,0.000758511,0.002241687,0.000507171,0.003545833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006036993,"about_ca_system_score_gemma":0.001652478,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0234258,"about_ca_topic_score_gemma":0.02055195,"domain_scores_codex":[0.9970227,0.0007397356,0.0002856143,0.0007241035,0.00073358,0.0004942371],"domain_scores_gemma":[0.993369,0.001696973,0.0019048,0.001271176,0.001427188,0.0003309171],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009626358,0.0001111686,0.6977789,0.002561682,0.0008848465,0.0005575343,0.00126661,0.002071985,0.003853045,0.006487786,0.2004916,0.08297223],"study_design_scores_gemma":[0.00006740638,0.00004002927,0.7346737,0.0008353796,0.0002762258,0.0008253751,0.002606423,0.00115498,0.004792598,0.002160305,0.2525133,0.00005430561],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.2037404,0.004002403,0.002538469,0.001330421,0.0001337011,0.0000672055,0.7601944,0.0004162112,0.02757675],"genre_scores_gemma":[0.4738553,0.002472453,0.003752615,0.000994252,0.0001167691,0.0002485314,0.5145684,0.0002167719,0.00377488],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9983996,"threshold_uncertainty_score":0.04657888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03008489034187926,"score_gpt":0.2558797841081827,"score_spread":0.2257948937663035,"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."}}