{"id":"W2771627735","doi":"10.2196/mhealth.9035","title":"Detecting Smoking Events Using Accelerometer Data Collected Via Smartwatch Technology: Validation Study","year":2017,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Smoking Behavior and Cessation","field":"Medicine","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Center for Research Resources; National Institute of General Medical Sciences; National Cancer Institute","keywords":"Smartwatch; Context (archaeology); Session (web analytics); Accelerometer; Medicine; Machine learning; Computer science; Wearable computer; World Wide Web; Embedded system","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002968562,0.001011749,0.0005815441,0.0006160581,0.0002502199,0.0003239948,0.000809098,0.001022296,0.000576532],"category_scores_gemma":[0.004557437,0.0003127321,0.0008613697,0.0003609617,0.0003693212,0.0004547889,0.0004735082,0.0004039382,0.0005038458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002114831,"about_ca_system_score_gemma":0.0004050149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002931808,"about_ca_topic_score_gemma":0.003575826,"domain_scores_codex":[0.9987264,0.0005523888,0.0001189832,0.0002181211,0.0002897333,0.00009429859],"domain_scores_gemma":[0.99641,0.001454809,0.0003174033,0.0005889971,0.001073129,0.0001557449],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002281909,0.009340849,0.8314348,0.0003471595,0.0008104309,0.0003341848,0.0007843947,0.01441803,0.04026822,0.0001878908,0.0008424878,0.09894978],"study_design_scores_gemma":[0.0004844422,0.02025094,0.8274318,0.00008090956,0.0004660182,0.0007682549,0.0005243444,0.128213,0.01983979,0.0001916164,0.001682027,0.00006680786],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942955,0.00005601628,0.004873291,0.00001530868,0.00001221812,0.0001784947,0.0002873184,0.0000651565,0.0002166838],"genre_scores_gemma":[0.9917423,0.00008170928,0.006151075,0.00002765251,0.00001198298,0.0001885527,0.001491576,0.000008957647,0.000296275],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002968562,"threshold_uncertainty_score":0.01569945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2320746621638708,"score_gpt":0.4674736154560858,"score_spread":0.235398953292215,"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."}}