{"id":"W2399294503","doi":"10.1007/978-3-319-19992-4_63","title":"Functional Nonlinear Mixed Effects Models for Longitudinal Image Data","year":2015,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"National Center for Research Resources; National Center for Advancing Translational Sciences; National Institute of Biomedical Imaging and Bioengineering; National Institute of Mental Health","keywords":"Covariance operator; Computer science; Nonlinear system; Covariance; Functional data analysis; Artificial intelligence; Random effects model; Covariate; Algorithm; Machine learning; Data mining; Statistics; Mathematics","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":[0.01386126,0.002241282,0.002830711,0.002001403,0.0008670412,0.002406495,0.00508386,0.003851924,0.00647114],"category_scores_gemma":[0.02975559,0.002799838,0.004217316,0.002807855,0.00173841,0.003035535,0.00279399,0.004443411,0.001913554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002078869,"about_ca_system_score_gemma":0.002759249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01679187,"about_ca_topic_score_gemma":0.02368437,"domain_scores_codex":[0.9957076,0.002719135,0.0002069087,0.0008415495,0.0002706242,0.0002541316],"domain_scores_gemma":[0.9801853,0.01614111,0.001020534,0.001383959,0.0009706174,0.0002983982],"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":[0.001370664,0.0003481484,0.006916215,0.0008729637,0.002431404,0.0005884169,0.0006332204,0.6519982,0.003275916,0.1654305,0.008357375,0.1577771],"study_design_scores_gemma":[0.0000645027,0.00008397453,0.000851242,0.00004426894,0.0001765347,0.0001057726,0.0000266651,0.9331301,0.0004102173,0.06213618,0.002919678,0.00005078422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005364595,0.001059628,0.9914721,0.0004711423,0.000116509,0.00006809576,0.000693447,0.0004883725,0.0002661877],"genre_scores_gemma":[0.2743838,0.0041397,0.6927731,0.0005852839,0.000839535,0.002547379,0.00577514,0.0009589098,0.01799713],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01679187,"threshold_uncertainty_score":0.07330626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1640109882639426,"score_gpt":0.3835420514656848,"score_spread":0.2195310632017422,"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."}}