{"id":"W2963672622","doi":"10.1037/met0000175","title":"Alternating optimization for G × E modelling with weighted genetic and environmental scores: Examples from the MAVAN study.","year":2018,"lang":"en","type":"article","venue":"Psychological Methods","topic":"Mental Health Research Topics","field":"Psychology","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Jewish General Hospital","funders":"","keywords":"Linear model; Statistics; Genetic algorithm; Computer science; Construct (python library); Mixed model; R package; Econometrics; Mathematics; Machine learning","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":[0.004525127,0.001108284,0.001175611,0.0005801138,0.0007856738,0.001023618,0.00192011,0.00208356,0.01025528],"category_scores_gemma":[0.0248045,0.0005698786,0.00129865,0.001201004,0.001009118,0.001368047,0.001792914,0.002235031,0.0007489742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001106406,"about_ca_system_score_gemma":0.001426525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.018183,"about_ca_topic_score_gemma":0.02810176,"domain_scores_codex":[0.9980224,0.001651737,0.00004171731,0.0001548932,0.0000666818,0.00006257035],"domain_scores_gemma":[0.986037,0.01272341,0.0003845531,0.0003914043,0.0002679467,0.0001956492],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000103095,0.00006085091,0.002128874,0.00008359321,0.0001150966,0.0001630239,0.0001966259,0.8979198,0.0001822691,0.08327396,0.00194321,0.01382963],"study_design_scores_gemma":[0.00004239535,0.00002807223,0.0003460439,0.00001894075,0.00002230159,0.00002901171,0.00003831888,0.9255276,0.00006036465,0.07238569,0.001486885,0.00001437838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05951221,0.0005863991,0.9266604,0.001654244,0.0001093039,0.0001936519,0.0005348554,0.0002730904,0.01047585],"genre_scores_gemma":[0.5227913,0.0005796976,0.4624213,0.0006305692,0.00007677346,0.001257072,0.0006341003,0.0002376185,0.0113716],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.018183,"threshold_uncertainty_score":0.03615433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.257036098063129,"score_gpt":0.5134929462929112,"score_spread":0.2564568482297822,"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."}}