{"id":"W363129665","doi":"10.69645/yqlf1627","title":"Consanguinity and genomic sharing in human evolutionary inference","year":2015,"lang":"en","type":"article","venue":"˜The œbiomedical & life sciences collection.","topic":"Race, Genetics, and Society","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Inference; Consanguinity; Evolutionary biology; Biology; Genetics; Computer science; Artificial intelligence","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":[0.0009139095,0.00009838638,0.0001073235,0.00006213678,0.0005410669,0.00004365072,0.0003225774,0.000124338,0.00001862633],"category_scores_gemma":[0.0002709969,0.00007694751,0.00003615686,0.0004277149,0.001532148,0.000006820392,0.0002562097,0.0001296158,0.00000527985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004394491,"about_ca_system_score_gemma":0.0005169878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002487236,"about_ca_topic_score_gemma":0.0003507398,"domain_scores_codex":[0.998815,0.00007902694,0.000210351,0.0003820646,0.0002697855,0.0002437883],"domain_scores_gemma":[0.9994419,0.00003289934,0.00005566149,0.0001886997,0.00006301775,0.0002178394],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001573678,0.0007154206,0.6442624,0.00005997985,0.0001395569,0.00001267239,0.006589336,0.001197365,0.15047,0.002284206,0.1917744,0.002337357],"study_design_scores_gemma":[0.006868425,0.004177154,0.6081644,0.00009594622,0.00007804221,0.0001961637,0.01660307,0.03475517,0.003057337,0.028435,0.295747,0.001822266],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99425,0.001686382,0.0005415748,0.0005419439,0.0003296981,0.0001472929,0.000004415363,0.00001243738,0.002486231],"genre_scores_gemma":[0.9976653,0.00015255,0.0003798657,0.0004694051,0.0002808626,0.00001847242,0.00001092589,0.000005083261,0.001017481],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1474127,"threshold_uncertainty_score":0.5645265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03144220939742364,"score_gpt":0.3021312717547494,"score_spread":0.2706890623573258,"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."}}