{"id":"W3129747896","doi":"10.2196/24465","title":"Predicting Emotional States Using Behavioral Markers Derived From Passively Sensed Data: Data-Driven Machine Learning Approach","year":2021,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Digital Mental Health Interventions","field":"Psychology","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning; Psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006435724,0.0002988029,0.0004658121,0.0001254337,0.0006681083,0.0001268892,0.0004562981,0.0001684984,0.0002730948],"category_scores_gemma":[0.00006826383,0.0003184433,0.00005201315,0.0002866115,0.0001396416,0.00058127,0.0007581136,0.0007746057,0.00003040895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002152957,"about_ca_system_score_gemma":0.0005443192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006075855,"about_ca_topic_score_gemma":0.000726012,"domain_scores_codex":[0.9957778,0.0007418835,0.0009261343,0.001285484,0.0003767852,0.0008919044],"domain_scores_gemma":[0.997364,0.0002344072,0.0005118854,0.001074931,0.0001010961,0.0007136594],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002568035,0.009251878,0.7782278,0.005477437,0.0006432015,0.0007131855,0.01106617,0.0006250108,0.000257573,0.00121695,0.01322178,0.176731],"study_design_scores_gemma":[0.005212669,0.001084285,0.7027504,0.0005968978,0.0003927176,0.000506088,0.009429961,0.2733225,0.00001485511,0.0003615193,0.005499639,0.0008284549],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9842657,0.001646948,0.001854421,0.001341024,0.001035061,0.0005836838,0.00857233,0.000166177,0.0005346868],"genre_scores_gemma":[0.9598718,0.00007651043,0.0112228,0.0005873486,0.0003290854,0.00003627813,0.02744306,0.00006403926,0.0003691134],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2726975,"threshold_uncertainty_score":0.9999267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1989393902563772,"score_gpt":0.4527604802422862,"score_spread":0.2538210899859089,"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."}}