{"id":"W2502353846","doi":"10.2196/resprot.5551","title":"Predicting Negative Emotions Based on Mobile Phone Usage Patterns: An Exploratory Study","year":2016,"lang":"en","type":"article","venue":"JMIR Research Protocols","topic":"Digital Mental Health Interventions","field":"Psychology","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Department of Health, Taipei City Government; Taipei City Government","keywords":"Mobile phone; Computer science; Machine learning; Feature selection; Artificial intelligence; Anxiety; Negative emotion; Applied psychology; Android (operating system); Psychology; Naive Bayes classifier; Phone; Social psychology; Support vector machine","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":[0.002822246,0.0005680394,0.0005011839,0.0004934321,0.0003609151,0.0005782995,0.0003953449,0.0004720505,0.0008203037],"category_scores_gemma":[0.00703886,0.0002728306,0.0006175308,0.0002791311,0.0002833029,0.0005083387,0.0004533041,0.0004463024,0.0004099895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001852946,"about_ca_system_score_gemma":0.0002412951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001017624,"about_ca_topic_score_gemma":0.001332186,"domain_scores_codex":[0.9990234,0.0005163276,0.00007104269,0.0001653384,0.0001430249,0.00008076199],"domain_scores_gemma":[0.9948735,0.003573402,0.0004203214,0.0003038517,0.0006021826,0.000226776],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001312213,0.006700587,0.930407,0.0002496607,0.0002629831,0.0007427941,0.009315876,0.001489163,0.00533753,0.00009419525,0.0006008123,0.04348706],"study_design_scores_gemma":[0.0001614175,0.01320627,0.9429078,0.00008823448,0.0003812775,0.001268611,0.01019187,0.02581193,0.004129197,0.0002523599,0.001526438,0.00007469691],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"protocol","genre_scores_codex":[0.9992513,0.00001685955,0.0004949018,0.000008251491,0.000001336201,0.00006778316,0.00006418682,0.00000494233,0.00009048417],"genre_scores_gemma":[0.9975827,0.00004426864,0.001755169,0.00002648837,0.000006964696,0.0001588566,0.0002231967,0.000004796119,0.0001974703],"genre_candidate":"protocol","genre_consensus":null,"teacher_disagreement_score":0.002822246,"threshold_uncertainty_score":0.01492566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.337974909398682,"score_gpt":0.5964163310996441,"score_spread":0.2584414217009621,"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."}}