{"id":"W2969872083","doi":"10.1186/s40168-019-0729-z","title":"An expectation-maximization algorithm enables accurate ecological modeling using longitudinal microbiome sequencing data","year":2019,"lang":"en","type":"article","venue":"Microbiome","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Genome Institute of Singapore; Agency for Science, Technology and Research","keywords":"Inference; Bottleneck; Microbiome; Profiling (computer programming); Computer science; Ecology; Microbial ecology; Maximization; Biology; Computational biology; Machine learning; Artificial intelligence; Bioinformatics; Mathematical optimization; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002986493,0.0002860225,0.0002735872,0.0001550084,0.0002275256,0.0001242747,0.0006403988,0.0002944961,0.0001343077],"category_scores_gemma":[0.000018855,0.0002844579,0.0000659388,0.000252252,0.00006060054,0.00005974615,0.0003380469,0.0001398054,0.00007407041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001377954,"about_ca_system_score_gemma":0.0003173096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001521682,"about_ca_topic_score_gemma":0.00005168435,"domain_scores_codex":[0.9978733,0.0001098494,0.0004214368,0.0009702815,0.00008366267,0.0005414651],"domain_scores_gemma":[0.9985526,0.000009759042,0.000162139,0.000993051,0.000159489,0.0001230192],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002529782,0.00008600208,0.0006912311,0.00004054939,0.00003698752,0.000005656256,0.00008416179,0.008282083,0.9901654,0.000004613696,0.0001619472,0.0004160725],"study_design_scores_gemma":[0.002402766,0.0006791037,0.001422192,0.0001374947,0.0001156099,0.0006160567,0.001176012,0.6424636,0.3455355,0.00004610821,0.003811374,0.001594184],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9141594,0.0002974615,0.08445866,0.00004546957,0.0003080279,0.0003559201,0.0003056895,0.00004105992,0.00002833302],"genre_scores_gemma":[0.9493633,0.00007348852,0.04430693,0.0002299201,0.0002512713,0.000004373631,0.005585201,0.00004965731,0.0001358767],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6446299,"threshold_uncertainty_score":0.9999608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06367987915202214,"score_gpt":0.324300097939376,"score_spread":0.2606202187873539,"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."}}