{"id":"W2075527847","doi":"10.1159/000324177","title":"Coordinated Conditional Simulation with SLINK and SUP of Many Markers Linked or Associated to a Trait in Large Pedigrees","year":2011,"lang":"en","type":"article","venue":"Human Heredity","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research","funders":"National Institutes of Health; University of Pittsburgh","keywords":"Pedigree chart; Linkage (software); Trait; Genetics; Quantitative trait locus; Locus (genetics); Genetic linkage; Software; Computer science; Identity by descent; R package; Computational biology; Biology; Genotype; Gene; Haplotype; Programming language","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":[0.0003691578,0.000102487,0.0001927427,0.00005183307,0.00006003358,0.000003803518,0.00006918814,0.0001776921,0.0001834569],"category_scores_gemma":[0.0002428856,0.00008798604,0.00002928344,0.00009090971,0.000056856,0.000003613713,0.00004199538,0.00007341168,0.000001325561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001824965,"about_ca_system_score_gemma":0.0000443462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001231691,"about_ca_topic_score_gemma":0.002096977,"domain_scores_codex":[0.9991555,0.0001142108,0.0002459125,0.0002216725,0.00007016271,0.0001926],"domain_scores_gemma":[0.9995455,0.00004153772,0.0001228348,0.000112819,0.0001172382,0.00006009361],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005023897,0.0003267288,0.9906297,0.00002417722,0.000130997,0.000007133226,0.000501474,0.001037369,0.004707871,0.0002082639,0.001743258,0.0001806235],"study_design_scores_gemma":[0.00119302,0.0006599964,0.9959099,0.00001411486,0.00001815073,0.00000169318,0.0001601249,0.001221326,0.0001753814,0.0002530936,0.0002758988,0.0001172662],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976669,0.00001477285,0.001380815,0.00003342237,0.00002233585,0.0001995029,0.0001439429,0.000007788939,0.000530498],"genre_scores_gemma":[0.9985465,0.000003689272,0.0005118195,0.00009621833,0.0000522694,0.0000176193,0.0003979797,0.00001041602,0.0003634737],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005280219,"threshold_uncertainty_score":0.3587967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03868824507270785,"score_gpt":0.2917879819860413,"score_spread":0.2530997369133334,"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."}}