{"id":"W2732106748","doi":"10.1093/geroni/igx004.4651","title":"INTEGRATIVE ANALYSIS OF LONGITUDINAL STUDIES ON AGING AND DEMENTIA (IALSA)","year":2017,"lang":"en","type":"article","venue":"Innovation in Aging","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Construct (python library); Dementia; Longitudinal study; Harmonization; Selection (genetic algorithm); Longitudinal data; Psychology; Gerontology; Data science; Econometrics; Computer science; Statistics; Medicine; Artificial intelligence; Data mining; Mathematics; Pathology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001416619,0.00005217736,0.0001984333,0.0004472279,0.0005679469,0.00007439196,0.000096007,0.00002353911,0.00001695172],"category_scores_gemma":[0.001255952,0.00004620741,0.00001960591,0.0007027707,0.00018518,0.0002498291,0.00003986905,0.00007239836,4.822587e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006216546,"about_ca_system_score_gemma":0.00004504983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003196854,"about_ca_topic_score_gemma":0.01240814,"domain_scores_codex":[0.9992001,0.00005815374,0.0003201645,0.0001169823,0.0001651995,0.0001393714],"domain_scores_gemma":[0.9992107,0.0001782481,0.0003000241,0.0001055045,0.0001904095,0.00001518643],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000002019218,0.00000647974,0.8286914,0.00002359077,0.0001238309,8.268636e-7,0.01571084,0.0000247491,0.000005272875,0.151592,0.00004174365,0.003777168],"study_design_scores_gemma":[0.0001133812,0.000006025303,0.9749333,0.0001429845,0.00005579259,2.20325e-8,0.02167171,0.000123263,0.000106689,0.002062649,0.000727663,0.00005652596],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9884116,0.0003137498,0.0001796478,0.005767745,0.0002451888,0.00007124661,0.00000237925,0.000008736617,0.004999721],"genre_scores_gemma":[0.999114,0.0002006698,0.000148099,0.0003835684,0.00004667953,0.000005634061,0.000001739503,0.000002036368,0.00009751903],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1495294,"threshold_uncertainty_score":0.6924036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1268769787280993,"score_gpt":0.4620403726548065,"score_spread":0.3351633939267072,"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."}}