{"id":"W2323260053","doi":"10.1037/a0035297","title":"The separation of between-person and within-person components of individual change over time: A latent curve model with structured residuals.","year":2013,"lang":"en","type":"article","venue":"Journal of Consulting and Clinical Psychology","topic":"Mental Health Research Topics","field":"Psychology","cited_by":487,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"National Institute on Drug Abuse; National Institutes of Health","keywords":"Psychology; Latent variable model; Set (abstract data type); Stability (learning theory); Reciprocal; Latent variable; Latent growth modeling; Growth curve (statistics); Multivariate statistics; Regression; Statistical model; Cognitive psychology; Artificial intelligence; Machine learning; Econometrics; Developmental psychology; Computer science; Mathematics; Psychotherapist","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":[0.0025658,0.0001324083,0.000523843,0.0001004297,0.0001000174,0.00002026101,0.0002015803,0.0002233779,0.00005775349],"category_scores_gemma":[0.000238648,0.00008106205,0.00006131802,0.0001013144,0.0006074025,0.00009780731,0.00003324464,0.0006825533,0.000003701029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001169373,"about_ca_system_score_gemma":0.00004400516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007174295,"about_ca_topic_score_gemma":0.00001546277,"domain_scores_codex":[0.9973611,0.0007585872,0.001035065,0.0002142354,0.000367577,0.0002634716],"domain_scores_gemma":[0.9967455,0.001279717,0.001293308,0.0002038179,0.0002769449,0.0002007231],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003569251,0.0004510589,0.8678356,0.0001202929,0.000961108,0.00001661282,0.007938044,0.000006238032,0.001454444,0.0004362717,0.004826253,0.1123848],"study_design_scores_gemma":[0.004953985,0.002382444,0.9902616,0.0001328731,0.00009201688,0.00007685847,0.0005902016,0.0009077133,0.00003014819,0.0004191899,0.00006347657,0.00008945467],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961831,0.0007412817,0.00003967204,0.002032232,0.0002425595,0.0003743804,0.00002603129,0.000004171845,0.0003565533],"genre_scores_gemma":[0.9980311,0.0001492968,0.001115019,0.0003412729,0.0001507563,0.000006255178,0.000005209372,0.00001278884,0.000188263],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.122426,"threshold_uncertainty_score":0.3305615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3070934735097968,"score_gpt":0.5104513217750134,"score_spread":0.2033578482652166,"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."}}