{"id":"W2531046897","doi":"10.1002/sta4.121","title":"Longitudinal functional additive model with continuous proportional outcomes for physical activity data","year":2016,"lang":"en","type":"article","venue":"Stat","topic":"Behavioral Health and Interventions","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Cancer Institute","keywords":"Variance (accounting); Functional data analysis; Functional principal component analysis; Statistics; Random effects model; Mathematics; Regression; Principal component analysis; Regression analysis; Econometrics; Computer science; Medicine; Economics","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":[0.02852828,0.002255813,0.00313316,0.002821455,0.001527818,0.002837863,0.006547727,0.003510971,0.01114335],"category_scores_gemma":[0.03939316,0.001248001,0.003437351,0.003258699,0.002137128,0.002496497,0.002832369,0.004099616,0.002821763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002164586,"about_ca_system_score_gemma":0.003186389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02133452,"about_ca_topic_score_gemma":0.01355792,"domain_scores_codex":[0.9879493,0.007607004,0.0004735469,0.00214295,0.001004656,0.0008226599],"domain_scores_gemma":[0.97657,0.01671766,0.002232652,0.00183531,0.002133845,0.000510697],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002278536,0.001665727,0.1574942,0.0007525217,0.00193628,0.001867145,0.002606218,0.5630414,0.0016505,0.173075,0.01190418,0.08172813],"study_design_scores_gemma":[0.0002146115,0.0005694118,0.01213641,0.00007630004,0.0002808687,0.0003083669,0.0002662168,0.9513116,0.0002524623,0.03102964,0.003445154,0.000108846],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2150998,0.001281219,0.7681087,0.003182163,0.0004005666,0.001029796,0.006252825,0.001285082,0.003359786],"genre_scores_gemma":[0.8000649,0.001236504,0.1633846,0.0007592215,0.0004306559,0.004961009,0.007354715,0.0002017777,0.02160669],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02852828,"threshold_uncertainty_score":0.1508737,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2160856631745533,"score_gpt":0.450265786182727,"score_spread":0.2341801230081737,"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."}}