{"id":"W1976616942","doi":"10.1086/430472","title":"In Silico Analysis of Disease-Association Mapping Strategies Using the Coalescent Process and Incorporating Ascertainment and Selection","year":2005,"lang":"en","type":"article","venue":"The American Journal of Human Genetics","topic":"Genetic Mapping and Diversity in Plants and Animals","field":"Biochemistry, Genetics and Molecular Biology","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Human Genome Research Institute; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Coalescent theory; In silico; Selection (genetic algorithm); Computational biology; Genetics; Biology; Evolutionary biology; Computer science; Artificial intelligence; Gene; Phylogenetic tree","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01155519,0.001194429,0.001629922,0.001523825,0.001056399,0.001359002,0.002462388,0.001559202,0.002667416],"category_scores_gemma":[0.01869714,0.0007725695,0.002249144,0.001204413,0.0004851133,0.001200586,0.0008335102,0.001651322,0.000435999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008160014,"about_ca_system_score_gemma":0.001720139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002568071,"about_ca_topic_score_gemma":0.004504288,"domain_scores_codex":[0.9972333,0.002118677,0.0001250333,0.0002705982,0.0001629469,0.00008934145],"domain_scores_gemma":[0.9784742,0.01998938,0.0003683598,0.0004630349,0.0003842767,0.0003207815],"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":[0.001645324,0.0009204364,0.02959999,0.0005095899,0.002162674,0.00117408,0.0005874173,0.8854247,0.01811421,0.0214458,0.0009736229,0.03744204],"study_design_scores_gemma":[0.0002001478,0.0001061948,0.0009886922,0.000007885324,0.0002678488,0.0001394602,0.00002048538,0.9921708,0.001892908,0.003873457,0.0003113451,0.00002086469],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5229742,0.0004351483,0.4728549,0.0004799023,0.00006806209,0.0001764096,0.0006128255,0.001446875,0.000951644],"genre_scores_gemma":[0.7769586,0.0002126653,0.2206363,0.0001445478,0.00003006377,0.0002104232,0.001103755,0.0002739321,0.0004297818],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01155519,"threshold_uncertainty_score":0.06111044,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01265390890900264,"score_gpt":0.2741885693092821,"score_spread":0.2615346604002795,"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."}}