{"id":"W2781687697","doi":"10.1101/234153","title":"Simulating Pedigrees Ascertained for Multiple Disease-Affected Relatives","year":2017,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"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); Genetics; Psychology; Medicine; Biology; Computer science; Machine learning; Internal medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003086834,0.0003592205,0.0006879593,0.0006463572,0.0003752035,0.0006785257,0.001193313,0.001052784,0.004338193],"category_scores_gemma":[0.01603963,0.000344785,0.0007876045,0.000894279,0.0005700263,0.0005451358,0.0007474109,0.000844805,0.000258065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001116403,"about_ca_system_score_gemma":0.0009131139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02122473,"about_ca_topic_score_gemma":0.01493826,"domain_scores_codex":[0.9991515,0.0005678848,0.00003123569,0.0001304278,0.00005353511,0.00006555219],"domain_scores_gemma":[0.9831361,0.01434095,0.000749413,0.0007649317,0.0005115136,0.0004972173],"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":[0.0002901449,0.00006621646,0.02529874,0.00003982826,0.00009451083,0.000238549,0.0001229563,0.9631702,0.0002602271,0.005809288,0.001017589,0.003591654],"study_design_scores_gemma":[0.0001538145,0.00004455741,0.002447828,0.00001593245,0.000041552,0.00008034826,0.00006027226,0.9910246,0.0001956475,0.005298873,0.0006251162,0.00001153471],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9321448,0.0003297274,0.0609234,0.0008377964,0.00005823485,0.00009786135,0.002303799,0.0003585038,0.002945846],"genre_scores_gemma":[0.9780271,0.000127802,0.01832969,0.0001464769,0.00001497635,0.0001269219,0.001848379,0.00004502535,0.001333722],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02122473,"threshold_uncertainty_score":0.04220241,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01528931552732801,"score_gpt":0.2463605358122534,"score_spread":0.2310712202849254,"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."}}