{"id":"W2094317540","doi":"10.1159/000099829","title":"Imputation of Missing Ages in Pedigree Data","year":2007,"lang":"en","type":"article","venue":"Human Heredity","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; Ontario Institute for Cancer Research","funders":"","keywords":"Imputation (statistics); Missing data; Statistics; Regression; Pedigree chart; Regression analysis; Linear regression; Mathematics; Medicine; Computer science; Biology; Genetics","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":[0.001350022,0.00006680705,0.0001719394,0.000052961,0.00003647742,0.00001225399,0.0002000277,0.0000526302,0.0001679939],"category_scores_gemma":[0.001540739,0.0000612881,0.00001475771,0.00009460012,0.00006044105,0.00008650826,0.00008268074,0.0001104396,0.000002539766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002231676,"about_ca_system_score_gemma":0.00001664321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001138835,"about_ca_topic_score_gemma":0.0003332965,"domain_scores_codex":[0.9991406,0.00006400887,0.0003312069,0.0001617632,0.000156598,0.0001458019],"domain_scores_gemma":[0.9984221,0.001031679,0.000113303,0.0003512236,0.00004059634,0.00004106878],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002827251,0.0003166429,0.0189397,0.0004982387,0.00001387676,0.00009787515,0.001069635,2.028346e-7,0.002567631,0.5156946,0.00368094,0.4570923],"study_design_scores_gemma":[0.0001823623,0.00002954036,0.1170339,0.00006724885,0.00001042955,0.000003045277,0.00006719342,0.0003273136,0.0006048027,0.8815374,0.00006507801,0.00007167369],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1752496,0.00002503633,0.8197587,0.00002370661,0.00009695584,0.0000861076,0.00003743235,0.00002360984,0.004698916],"genre_scores_gemma":[0.5447706,7.795795e-7,0.4550627,0.000008418417,0.0001198227,4.998883e-7,0.00001498205,0.000006234673,0.0000159648],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4570207,"threshold_uncertainty_score":0.2499256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2951842863507974,"score_gpt":0.4897296952113052,"score_spread":0.1945454088605078,"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."}}