{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003219322,0.0001113419,0.0001165333,0.00001415926,0.0001113608,0.00002201104,0.0001671577,0.00006320737,0.008158608],"category_scores_gemma":[0.00001134,0.00009214444,0.00001616349,0.00006285508,0.0001598026,0.0004364802,0.0005158439,0.0001256444,0.0005274334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009798597,"about_ca_system_score_gemma":0.000004791158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004906593,"about_ca_topic_score_gemma":0.00001237482,"domain_scores_codex":[0.9987232,0.00008197123,0.000131605,0.0006138852,0.0002075556,0.0002417908],"domain_scores_gemma":[0.9994389,0.00002685044,0.0000430503,0.0003985091,0.000001413326,0.0000912851],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001968799,0.0001599695,0.5613132,0.00001168821,0.000009522427,0.000006024554,0.0004240194,0.0001734303,0.4267555,0.00004933046,0.0006721078,0.01040552],"study_design_scores_gemma":[0.001220816,0.000218622,0.8566411,0.00004408456,0.00006231648,0.00003011005,0.001119852,0.1160235,0.001023324,0.0002372957,0.02292119,0.0004577914],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9938607,0.00002279441,0.001723759,0.0000952343,0.00005264593,0.0002994095,0.00001770866,0.00004161267,0.003886152],"genre_scores_gemma":[0.9938273,0.00005024614,0.00485596,0.0004123716,0.00003009437,0.00000347078,0.00002683972,0.00001372719,0.0007799692],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4257322,"threshold_uncertainty_score":0.9927481,"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."}}