{"id":"W2953813907","doi":"10.1016/j.ekir.2019.05.744","title":"SUN-334 SINGLE-CELL SEQUENCING AND ARTIFICIAL INTELLIGENCE: KIDNEY MEDICINE IN THE DIGITAL AGE AND BEYOND","year":2019,"lang":"en","type":"article","venue":"Kidney International Reports","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta Hospital","funders":"","keywords":"Computational biology; DNA sequencing; Cell; Gene; Organism; Medicine; Genetics; Biology","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":[0.001812757,0.0002878595,0.0006448575,0.000706648,0.0006149436,0.001945122,0.0006538169,0.001173473,0.006551128],"category_scores_gemma":[0.001554011,0.0002219556,0.0003384565,0.000869449,0.001431105,0.001822932,0.001412124,0.001188942,0.001717157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007886735,"about_ca_system_score_gemma":0.00110246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001107818,"about_ca_topic_score_gemma":0.001639329,"domain_scores_codex":[0.9994709,0.000126607,0.00002274509,0.0001108209,0.0002311159,0.0000377793],"domain_scores_gemma":[0.9989178,0.0003417688,0.00005778188,0.0001489748,0.0003033783,0.0002302908],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004776793,0.00006993294,0.004118904,0.001190444,0.0001048337,0.0003234803,0.00056776,0.005013296,0.1373931,0.1324486,0.07374629,0.6445457],"study_design_scores_gemma":[0.00009475496,0.0002719189,0.00645635,0.0004938801,0.00009360175,0.001293221,0.0005845012,0.03903597,0.09651407,0.192111,0.6628516,0.0001991744],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0829888,0.1468842,0.5699466,0.05566317,0.006155305,0.0001586113,0.002769087,0.007840507,0.1275937],"genre_scores_gemma":[0.4423511,0.08711974,0.3955677,0.01023382,0.002120391,0.000274392,0.002221246,0.001165316,0.05894639],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006551128,"threshold_uncertainty_score":0.02191567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01983451905485213,"score_gpt":0.2477278428842868,"score_spread":0.2278933238294346,"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."}}