{"id":"W2947971197","doi":"10.1093/acrefore/9780190236557.013.364","title":"Mixture Modeling for Lifespan Developmental Research","year":2019,"lang":"en","type":"reference-entry","venue":"Oxford Research Encyclopedia of Psychology","topic":"Advanced Statistical Modeling Techniques","field":"Computer Science","cited_by":93,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Concordia University","funders":"University of Oxford","keywords":"Mixture model; Moderation; Covariate; Latent growth modeling; Latent variable model; Similarity (geometry); Structural equation modeling; Statistical model; Econometrics; Contrast (vision); Latent variable; Local independence; Psychology; Statistics; Probabilistic logic; Sample (material); Nested set model; Mathematics; Computer science; Data mining; Artificial intelligence","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":[0.03398647,0.002178779,0.002963256,0.007286528,0.001678223,0.004038985,0.004215648,0.002724069,0.01459555],"category_scores_gemma":[0.07518028,0.001469862,0.005438385,0.007364428,0.002570273,0.003855243,0.00546131,0.004922833,0.002342997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003819108,"about_ca_system_score_gemma":0.003444198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01850472,"about_ca_topic_score_gemma":0.01366808,"domain_scores_codex":[0.9826187,0.01331915,0.0005518757,0.002053906,0.001089908,0.0003664024],"domain_scores_gemma":[0.9328783,0.05743515,0.002733671,0.003484676,0.002758465,0.0007096966],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001601621,0.0001834808,0.01117438,0.000641406,0.00134318,0.000378753,0.001449731,0.1234355,0.0003655534,0.7570541,0.007902771,0.09591106],"study_design_scores_gemma":[0.00003647511,0.0000626968,0.002106354,0.0002952612,0.0002004082,0.0001228017,0.0002085765,0.4530903,0.0001447746,0.533126,0.01054577,0.00006058104],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005185583,0.002052717,0.9880526,0.001008211,0.0001837724,0.0002014124,0.0008272404,0.0004427177,0.002045784],"genre_scores_gemma":[0.192138,0.004375347,0.787546,0.0006648136,0.0006126968,0.003396688,0.003363475,0.0005085921,0.007394412],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03398647,"threshold_uncertainty_score":0.1797398,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1882130719528156,"score_gpt":0.4782069564251146,"score_spread":0.289993884472299,"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."}}