{"id":"W2903777333","doi":"10.1101/491183","title":"A probabilistic model to identify the core microbial community","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Microbial population biology; Microbiome; Probabilistic logic; Biology; Species richness; Community structure; Metagenomics; Core (optical fiber); Ecology; Identification (biology); Computational biology; Computer science; Artificial intelligence; Bioinformatics; Genetics; Bacteria","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004978558,0.0009158725,0.000970227,0.002229763,0.0007659072,0.00151084,0.002761604,0.001824318,0.00209541],"category_scores_gemma":[0.01315968,0.000633865,0.001892649,0.001232971,0.001460173,0.002553299,0.0017193,0.001764811,0.0005941789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001285948,"about_ca_system_score_gemma":0.001489606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005520648,"about_ca_topic_score_gemma":0.003615731,"domain_scores_codex":[0.9976273,0.0008341065,0.0001185297,0.0006669792,0.0005256286,0.0002273979],"domain_scores_gemma":[0.9920248,0.005590374,0.0008385504,0.0004009277,0.0009019777,0.0002434472],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001910027,0.0001408753,0.01305575,0.0003127583,0.0001609936,0.0002501372,0.0002198101,0.877194,0.009479003,0.04472801,0.002111028,0.0521566],"study_design_scores_gemma":[0.000005282233,0.00001393925,0.000496963,0.000007676521,0.000007010527,0.0000339644,0.00000956644,0.9914723,0.0003809,0.007270269,0.0002904258,0.00001174681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.022053,0.0001604414,0.9764463,0.0002273046,0.00003413731,0.00007575551,0.0002242931,0.000256403,0.0005222924],"genre_scores_gemma":[0.6209266,0.0004113817,0.3727947,0.0004133878,0.0002122346,0.0006054065,0.001412703,0.0001669918,0.003056749],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005520648,"threshold_uncertainty_score":0.0263294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03674485050523726,"score_gpt":0.2887731357469369,"score_spread":0.2520282852416996,"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."}}