{"id":"W1590964346","doi":"10.1002/gepi.21733","title":"Strategy to Control Type I Error Increases Power to Identify Genetic Variation Using the Full Biological Trajectory","year":2013,"lang":"en","type":"article","venue":"Genetic Epidemiology","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; Ontario Institute for Cancer Research; University of Toronto","funders":"National Heart, Lung, and Blood Institute; National Institute on Aging; Canadian Institutes of Health Research","keywords":"Type I and type II errors; Residual; Power (physics); Statistical power; Computer science; Statistics; Mathematics; Algorithm","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001504519,0.0003924858,0.0006711142,0.0001203512,0.00025691,0.00002628602,0.0005935916,0.0005767522,0.0004628209],"category_scores_gemma":[0.004730398,0.00028971,0.000199765,0.0002961761,0.0001756654,0.000005073988,0.0002346533,0.0002123582,0.0004613828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006358062,"about_ca_system_score_gemma":0.0001700273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001249836,"about_ca_topic_score_gemma":0.0001612524,"domain_scores_codex":[0.9947799,0.00215789,0.001018097,0.0009303138,0.0001212705,0.0009925195],"domain_scores_gemma":[0.997419,0.0006722907,0.0003179809,0.0008457548,0.0003524533,0.0003925306],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0002126329,0.0001643569,0.1618355,0.00001028801,0.0003407792,0.000004671753,0.0001606388,0.1125464,0.695207,0.0001364157,0.0234153,0.005966012],"study_design_scores_gemma":[0.0004676635,0.001843358,0.990113,0.000007289681,0.00006288086,0.00007358257,0.0001657694,0.002401541,0.0002354835,0.001539168,0.002678774,0.0004114664],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8927969,0.001345038,0.1022754,0.00178269,0.0004867013,0.001128344,0.00003017769,0.00002841219,0.0001263041],"genre_scores_gemma":[0.9529718,0.0001062696,0.03654844,0.009308454,0.0005938867,0.00024285,0.00004711778,0.00004089199,0.0001402476],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8282775,"threshold_uncertainty_score":0.9999555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05527703173423414,"score_gpt":0.3424906221808142,"score_spread":0.2872135904465801,"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."}}