{"id":"W3047346431","doi":"10.2196/17818","title":"Using Machine Learning and Smartphone and Smartwatch Data to Detect Emotional States and Transitions: Exploratory Study","year":2020,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"University of Calgary","keywords":"Wearable computer; Context (archaeology); Artificial intelligence; Psychology; Computer science; Smartwatch; Cognitive psychology; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005569053,0.000171226,0.0002702628,0.0001134514,0.0004475428,0.00004912142,0.00005025905,0.00007273161,0.00007374207],"category_scores_gemma":[0.00003342818,0.0001666114,0.000009432795,0.0001759451,0.00006833726,0.0001671967,0.00008716468,0.0003223894,0.000007989813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001661611,"about_ca_system_score_gemma":0.00006645569,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002767834,"about_ca_topic_score_gemma":0.0006409461,"domain_scores_codex":[0.9982252,0.0004037731,0.000324617,0.0006002574,0.0001317225,0.0003143972],"domain_scores_gemma":[0.9989415,0.00009033741,0.00009103494,0.0001377366,0.00004043596,0.0006989074],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.003004134,0.001278762,0.3244005,0.00414844,0.0002654166,0.0001115176,0.2053376,0.00001890257,0.001119994,0.0003253605,0.004067629,0.4559217],"study_design_scores_gemma":[0.01191223,0.007290408,0.8997194,0.0002554793,0.0003287443,0.0003517016,0.05543255,0.01071348,0.00001793211,0.0005697382,0.01245331,0.0009550106],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.98917,0.002801144,0.001140539,0.00571245,0.0001214778,0.0007552732,0.0001824789,0.00007336529,0.00004333633],"genre_scores_gemma":[0.9935315,0.001540466,0.001254346,0.003257466,0.0001588479,0.00003154668,0.0001757733,0.00002523371,0.00002486324],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5753189,"threshold_uncertainty_score":0.6794214,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2168384361872594,"score_gpt":0.4314461148764102,"score_spread":0.2146076786891508,"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."}}