{"id":"W3048464602","doi":"10.1016/j.tim.2020.06.009","title":"Development of Microbiome Biobanks – Challenges and Opportunities","year":2020,"lang":"en","type":"article","venue":"Trends in Microbiology","topic":"Ethics in Clinical Research","field":"Medicine","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Agriculture and Agri-Food Canada","funders":"Horizon 2020; European Commission","keywords":"Biobank; Microbiome; Biology; Gut microbiome; Engineering ethics; Computational biology; Data science; Bioinformatics; Engineering; Computer science","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":[],"consensus_categories":[],"category_scores_codex":[0.0007814976,0.0001110079,0.0003955421,0.0002319661,0.0000183816,0.000002224783,0.0001301791,0.0003312647,0.0002115262],"category_scores_gemma":[0.0003395269,0.00009493456,0.00003824551,0.0001142482,0.0005037423,0.00001539717,0.0002136988,0.0005737284,0.00001350186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002680554,"about_ca_system_score_gemma":0.0001700935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003165052,"about_ca_topic_score_gemma":0.0001029087,"domain_scores_codex":[0.9988638,0.000070241,0.0004610958,0.0003077896,0.00006003653,0.0002370261],"domain_scores_gemma":[0.9990425,0.000537255,0.00006837316,0.0001588688,0.00007760228,0.0001154667],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002101005,0.0001231211,0.0004356052,0.0004376922,0.00006528017,0.00005056673,0.005363212,4.030776e-8,0.667568,0.004946261,0.0002683241,0.3205318],"study_design_scores_gemma":[0.01214639,0.003936146,0.07227672,0.001405944,0.0001243694,0.0005266355,0.008797228,0.00002639372,0.3393366,0.003149224,0.557077,0.001197334],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9487801,0.008640269,0.000008760913,0.03452106,0.0000686927,0.0001034255,0.00002584443,0.00003113285,0.007820757],"genre_scores_gemma":[0.9802555,0.007036966,0.01028636,0.00143729,0.00004278703,0.00000740394,0.00007060957,0.00001652271,0.0008466059],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5568087,"threshold_uncertainty_score":0.3871319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6973219109833081,"score_gpt":0.5173271363781033,"score_spread":0.1799947746052047,"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."}}