{"id":"W2900076889","doi":"10.1093/geroni/igy023.2907","title":"INTEGRATIVE DATA ANALYSIS OF LONGITUDINAL STUDIES: COORDINATED ANALYSIS AND MULTIPLE-STUDY REPLICATION RESEARCH","year":2018,"lang":"en","type":"article","venue":"Innovation in Aging","topic":"Aging and Gerontology Research","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Normative; Replication (statistics); Context (archaeology); Life span; Longitudinal data; Longitudinal study; Life course approach; Psychology; Data science; Computer science; Developmental psychology; Gerontology; Medicine; Epistemology; Data mining; Biology","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":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.5962399,0.00350563,0.009942134,0.01216007,0.006570945,0.01001953,0.006986849,0.003087013,0.00205594],"category_scores_gemma":[0.7753136,0.004336831,0.008822101,0.01470973,0.007586796,0.007414032,0.01187625,0.004482546,0.0005104425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005788879,"about_ca_system_score_gemma":0.0188983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005276211,"about_ca_topic_score_gemma":0.007816418,"domain_scores_codex":[0.231334,0.664643,0.04505027,0.02890513,0.02810014,0.001967475],"domain_scores_gemma":[0.1826455,0.5223155,0.03439921,0.1870495,0.07135028,0.002239936],"domain_codex":"methods","domain_gemma":"methods","domain_candidate":"methods","domain_consensus":"methods","study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003349505,0.001538659,0.1530728,0.03872286,0.05729558,0.001604503,0.07765962,0.01132678,0.009975236,0.07703532,0.02389059,0.5445286],"study_design_scores_gemma":[0.006136207,0.007845566,0.2333073,0.02380917,0.04107126,0.001921132,0.03768594,0.1112679,0.02040527,0.3889984,0.1253996,0.002152276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0352454,0.006344641,0.922904,0.002054826,0.001786528,0.02636583,0.0009813245,0.001600603,0.002716714],"genre_scores_gemma":[0.155716,0.0006763364,0.7989697,0.0004429622,0.0002315224,0.04257085,0.0006238227,0.0004881847,0.0002805712],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4037601,"threshold_uncertainty_score":0.4979082,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3879375778324511,"score_gpt":0.5746235505793635,"score_spread":0.1866859727469124,"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."}}