{"id":"W4230992016","doi":"10.1093/geront/gnv520.05","title":"STUDY PLANNING USING POWER ANALYSIS FOR LATENT GROWTH CURVE MODELS","year":2015,"lang":"en","type":"article","venue":"The Gerontologist","topic":"T-cell and B-cell Immunology","field":"Immunology and Microbiology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Latent growth modeling; Growth curve (statistics); Power (physics); Econometrics; Computer science; Statistics; Mathematics; Thermodynamics; Physics","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":[0.0008099467,0.0002419963,0.0005461056,0.0001363814,0.0003656507,0.00002821031,0.0005183479,0.0002295362,0.00005912801],"category_scores_gemma":[0.00008535648,0.0001554877,0.0002039864,0.0002485351,0.0003731622,0.0001010042,0.0001813357,0.0002669637,0.00007534845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008295018,"about_ca_system_score_gemma":0.00006040839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001433874,"about_ca_topic_score_gemma":0.0002418792,"domain_scores_codex":[0.9982492,0.0004067821,0.0003659171,0.0003906116,0.00004701693,0.0005405168],"domain_scores_gemma":[0.9988979,0.0002055078,0.0001955671,0.0005069781,0.0001651858,0.00002884025],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.006831076,0.005570599,0.4123637,0.00004116187,0.03211299,0.0002011393,0.06673896,0.0426727,0.3953612,0.01828389,0.01874845,0.001074049],"study_design_scores_gemma":[0.1089529,0.03310065,0.1331841,0.000219529,0.04921726,0.002366945,0.3092621,0.03087172,0.178626,0.1012976,0.04005479,0.01284635],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9778266,0.004595331,0.01441935,0.0002647375,0.0007950946,0.0005975637,0.00002511422,0.0001130758,0.001363126],"genre_scores_gemma":[0.9979388,0.000006054886,0.0002281281,0.0001156959,0.00002591436,0.00005342321,0.00005320645,0.0000169499,0.001561895],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2791796,"threshold_uncertainty_score":0.6340606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1454328564026615,"score_gpt":0.3200410149456834,"score_spread":0.1746081585430219,"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."}}