{"id":"W2973146878","doi":"10.1109/bhi.2019.8834456","title":"Personalized Wellbeing Prediction using Behavioral, Physiological and Weather Data","year":2019,"lang":"en","type":"article","venue":"","topic":"Health, Environment, Cognitive Aging","field":"Environmental Science","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Sleep & Circadian Network","funders":"","keywords":"Mood; Task (project management); Computer science; Machine learning; Artificial intelligence; Artificial neural network; Deep learning; Morning; Wearable computer; Predictive modelling; Data modeling; Psychology; Medicine; Clinical psychology; Engineering","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":[0.0008713399,0.0008097193,0.0004398532,0.0006256122,0.0001597132,0.0004373663,0.0003459986,0.0004692046,0.0006798736],"category_scores_gemma":[0.002493162,0.0001801345,0.0005866815,0.0004303829,0.0001070409,0.0005330261,0.0004370425,0.0006891441,0.0003069326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003397721,"about_ca_system_score_gemma":0.000308301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008626396,"about_ca_topic_score_gemma":0.01069004,"domain_scores_codex":[0.9997309,0.00008332026,0.00001567835,0.0001035443,0.0000303924,0.00003608816],"domain_scores_gemma":[0.9992894,0.0002929063,0.00008845008,0.00009530465,0.0001626607,0.00007126337],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001243501,0.001394092,0.2629528,0.000176022,0.0005046466,0.0002099297,0.0002938225,0.5365692,0.008811158,0.0004134004,0.003561279,0.1838701],"study_design_scores_gemma":[0.00002042068,0.0002753664,0.08087584,0.00001623763,0.00007474247,0.00003994546,0.00007291268,0.9147691,0.002274501,0.000893612,0.00065453,0.00003280232],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9425306,0.0003027056,0.05205669,0.0004289986,0.00009010101,0.00007958047,0.002776886,0.0007387349,0.0009957151],"genre_scores_gemma":[0.9857494,0.00007764406,0.0117464,0.00006539314,0.00002468162,0.00005000042,0.001730958,0.00001243506,0.0005430658],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008626396,"threshold_uncertainty_score":0.01715237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07965357150492283,"score_gpt":0.3170303897932387,"score_spread":0.2373768182883158,"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."}}