{"id":"W2115233244","doi":"10.1111/j.1752-4571.2010.00156.x","title":"The emergence of human‐evolutionary medical genomics","year":2010,"lang":"en","type":"article","venue":"Evolutionary Applications","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Biology; Population genomics; Genomics; Evolutionary biology; Natural selection; Population; Genetics; Human evolutionary genetics; Disease; Human genetics; Selection (genetic algorithm); Population genetics; Lineage (genetic); Balancing selection; Genetic variation; Phylogenetics; Genome; Gene; Machine learning; Computer science","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":[0.0003844473,0.00008995669,0.00009374721,0.00002332538,0.0005598086,0.000003355645,0.0004223436,0.0002044284,0.0001801193],"category_scores_gemma":[0.0002252742,0.00007639587,0.00008804185,0.0001231751,0.0003846151,0.000002440472,0.0001462761,0.000174621,0.00003743253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009591774,"about_ca_system_score_gemma":0.0002334694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002960732,"about_ca_topic_score_gemma":0.0001193085,"domain_scores_codex":[0.9990039,0.00005806936,0.0003432615,0.0002333517,0.0001635536,0.000197857],"domain_scores_gemma":[0.99901,0.00007344139,0.0001469338,0.0005079898,0.0001716118,0.00009],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002153039,0.0003483674,0.1040762,0.00001192661,0.000139765,3.678903e-7,0.00003483013,0.0002685499,0.5325536,0.1603457,0.1954061,0.006793095],"study_design_scores_gemma":[0.0001948079,0.00007323216,0.3828223,0.00000195496,0.00001809854,0.00002972842,0.00009459513,0.0007636076,0.001156078,0.01320341,0.6014728,0.0001694145],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9348363,0.003768089,0.03796312,0.00717797,0.0006441161,0.0009195502,0.0001696949,0.00004867499,0.01447245],"genre_scores_gemma":[0.992573,0.0005650951,0.004595306,0.0001168994,0.0004940362,0.0003173414,0.0002817688,0.00001172233,0.001044868],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5313975,"threshold_uncertainty_score":0.4305652,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007724641313241634,"score_gpt":0.277636836026499,"score_spread":0.2699121947132574,"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."}}