{"id":"W4385832586","doi":"10.1007/978-3-031-39821-6_31","title":"Toward Healthy Aging: Temporal Regression for Disability Prediction and Warning Decision-Making","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Warning system; Computer science; Population ageing; Regression; Regression analysis; Population; Healthy aging; Artificial intelligence; Machine learning; Gerontology; Demography; Medicine; Statistics; Mathematics; Telecommunications","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":[0.003160915,0.0008413711,0.0008174867,0.0006027073,0.0001774813,0.001206713,0.0009665235,0.0006900971,0.003933615],"category_scores_gemma":[0.00906028,0.0004343255,0.000694019,0.0009637443,0.000369623,0.001537784,0.0006810007,0.00182937,0.001343659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003884134,"about_ca_system_score_gemma":0.0008820776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0062979,"about_ca_topic_score_gemma":0.006484266,"domain_scores_codex":[0.9995017,0.0002571434,0.0000246351,0.0001030908,0.00008509008,0.00002836489],"domain_scores_gemma":[0.9968816,0.002577431,0.0001278522,0.0001181908,0.0002418381,0.00005312585],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001809712,0.0001667209,0.00477805,0.0002421654,0.000245562,0.0001074548,0.0001658651,0.3535844,0.001467575,0.07203472,0.02575013,0.5412765],"study_design_scores_gemma":[0.000005291916,0.00002285771,0.0004829554,0.0000231559,0.00002538621,0.00002061901,0.00001453156,0.9637463,0.0002296183,0.03278696,0.002633262,0.000008984896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007614569,0.002952076,0.9837047,0.001327252,0.0002025392,0.00001769551,0.000263319,0.0005388429,0.003379007],"genre_scores_gemma":[0.3082338,0.008105645,0.6585943,0.0006670234,0.001027538,0.0001622876,0.001368372,0.0004482689,0.02139284],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0062979,"threshold_uncertainty_score":0.01671666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04238820405075316,"score_gpt":0.3434833588677106,"score_spread":0.3010951548169574,"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."}}