{"id":"W1970546246","doi":"10.1534/genetics.114.162149","title":"Predicting Discovery Rates of Genomic Features","year":2014,"lang":"en","type":"article","venue":"Genetics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University and Génome Québec Innovation Centre","funders":"National Heart, Lung, and Blood Institute; Canada Research Chairs","keywords":"Jackknife resampling; Biology; Population; 1000 Genomes Project; Sample size determination; Exome sequencing; Exome; Selection (genetic algorithm); Computational biology; Estimator; Genome; Sample (material); Genetics; Computer science; Statistics; Single-nucleotide polymorphism; Mathematics; Machine learning; Mutation","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01969405,0.0006616247,0.001114449,0.002755137,0.0004933896,0.00141528,0.001466499,0.001808054,0.001138918],"category_scores_gemma":[0.07687884,0.0006216985,0.001090661,0.001814161,0.001193296,0.001763237,0.001209276,0.002154863,0.0005619381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000617436,"about_ca_system_score_gemma":0.0006617072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001413943,"about_ca_topic_score_gemma":0.001259548,"domain_scores_codex":[0.9946505,0.00288531,0.0002254561,0.001435047,0.0005414216,0.0002623755],"domain_scores_gemma":[0.8674877,0.1218506,0.004399505,0.004194068,0.001388228,0.0006799494],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007906844,0.0002345677,0.534271,0.0003511643,0.0008783874,0.001026009,0.0004868293,0.279909,0.008816062,0.01744943,0.003103583,0.1526833],"study_design_scores_gemma":[0.00007400533,0.0001631783,0.05549496,0.00005110755,0.0001906108,0.0007351557,0.0001212873,0.8841473,0.006616534,0.05043579,0.001910755,0.00005931424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6006958,0.001307934,0.3908855,0.001527009,0.00009175865,0.0001341907,0.00232682,0.0009618201,0.002069098],"genre_scores_gemma":[0.9234763,0.0004885869,0.07212973,0.0003155099,0.00009587919,0.0002297751,0.001974159,0.0001431823,0.00114674],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01969405,"threshold_uncertainty_score":0.1041533,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0091977577857734,"score_gpt":0.2564356339807236,"score_spread":0.2472378761949502,"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."}}