{"id":"W2952371697","doi":"10.1186/s13029-018-0069-6","title":"Simulating pedigrees ascertained for multiple disease-affected relatives","year":2018,"lang":"en","type":"article","venue":"Source Code for Biology and Medicine","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of British Columbia; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Pedigree chart; Anticipation (artificial intelligence); Disease; Identification (biology); Cluster (spacecraft); Computer science; Medicine; Genetics; Biology; Machine learning; Pathology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004497059,0.0001450953,0.000244083,0.00003868734,0.0003079612,0.000002477999,0.00008387754,0.0002199239,0.00001065841],"category_scores_gemma":[0.004766431,0.0001116976,0.00006563421,0.00004318774,0.0004661883,0.000001617864,0.00004710897,0.00005103633,0.000001126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006221838,"about_ca_system_score_gemma":0.00002931184,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001326477,"about_ca_topic_score_gemma":0.00009824125,"domain_scores_codex":[0.9989977,0.00008847552,0.0002288105,0.0003605025,0.00002660161,0.0002979116],"domain_scores_gemma":[0.9990077,0.0004083952,0.0001280018,0.0001657858,0.0001703782,0.0001197625],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001894716,0.0001164864,0.6447209,0.0001449408,0.0004896527,4.477019e-7,0.001089179,0.000369671,0.3062466,0.002100644,0.01705898,0.02576785],"study_design_scores_gemma":[0.01117327,0.009714025,0.3115148,0.0001048752,0.0003953161,0.00000762666,0.001100915,0.05527857,0.002732964,0.01182429,0.5953762,0.0007771322],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.876256,0.001226355,0.1196715,0.001796512,0.0002304397,0.0005509166,0.0001255448,0.00002262712,0.0001201832],"genre_scores_gemma":[0.9925337,0.0000783342,0.003199244,0.0005848188,0.00135891,0.000106036,0.0007785825,0.00001860978,0.001341719],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5783172,"threshold_uncertainty_score":0.5706207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02394165909793097,"score_gpt":0.3399856114480491,"score_spread":0.3160439523501181,"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."}}