{"id":"W2990567168","doi":"10.1038/s41598-019-53666-5","title":"Author Correction: A machine-learning heuristic to improve gene score prediction of polygenic traits","year":2019,"lang":"en","type":"erratum","venue":"Scientific Reports","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hamilton Health Sciences; University of Toronto; McMaster University; Population Health Research Institute","funders":"","keywords":"Heuristic; Computer science; Machine learning; Artificial intelligence; Polygene; Gene; Quantitative trait locus; Biology; Genetics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004128442,0.001995353,0.001370644,0.003511907,0.002811281,0.003058721,0.003125938,0.003042111,0.1132413],"category_scores_gemma":[0.08828244,0.0006428882,0.00127318,0.003133117,0.001521941,0.001858786,0.002022517,0.005449202,0.04687169],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00179829,"about_ca_system_score_gemma":0.003509964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008858183,"about_ca_topic_score_gemma":0.01461418,"domain_scores_codex":[0.9958618,0.000901392,0.0005994026,0.0008440792,0.001580409,0.0002128092],"domain_scores_gemma":[0.9578921,0.01342727,0.0009596951,0.004065488,0.02247831,0.001177156],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007837704,0.000009853131,0.0002067786,0.0001360358,0.00002584724,0.0003781359,0.00003616424,0.0002839329,0.0002116643,0.002488717,0.974496,0.02164856],"study_design_scores_gemma":[0.0001470871,0.0000416232,0.001143212,0.0003669175,0.0001104041,0.001649607,0.0001226455,0.003607912,0.001940199,0.01425395,0.9765278,0.0000886048],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.0008859475,0.0008637656,0.01514178,0.0375144,0.9294091,0.00006417262,0.003977545,0.001872077,0.01027116],"genre_scores_gemma":[0.0667114,0.00398736,0.09214216,0.0375121,0.1667455,0.0003110377,0.01213561,0.009960379,0.6104945],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1132413,"threshold_uncertainty_score":0.3788297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01507128207888684,"score_gpt":0.2659626605563548,"score_spread":0.250891378477468,"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."}}