{"id":"W2189564841","doi":"10.1109/acii.2015.7344575","title":"Predicting students' happiness from physiology, phone, mobility, and behavioral data","year":2015,"lang":"en","type":"article","venue":"","topic":"COVID-19 and Mental Health","field":"Psychology","cited_by":136,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada; Samsung; National Institutes of Health; National Heart, Lung, and Blood Institute; Brigham and Women's Hospital; Robert Wood Johnson Foundation","keywords":"Happiness; Affect (linguistics); Psychology; Applied psychology; Computer science; Artificial intelligence; Machine learning; Predictive power; Psychological intervention; Social psychology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0009184566,0.000595184,0.0005340728,0.0008715882,0.000234989,0.0007115632,0.0003939968,0.0005281788,0.001295127],"category_scores_gemma":[0.003264688,0.0001831913,0.0006131922,0.000606527,0.0001395337,0.0004644286,0.0006581685,0.0006775622,0.0007914308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002870445,"about_ca_system_score_gemma":0.0003115447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005334788,"about_ca_topic_score_gemma":0.006506791,"domain_scores_codex":[0.9996768,0.00009807816,0.00002533728,0.00009029586,0.00005362296,0.00005580203],"domain_scores_gemma":[0.9989767,0.00042111,0.000138446,0.0001245886,0.0002064224,0.0001327897],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000354174,0.001042423,0.8790895,0.00007310928,0.000195301,0.00008797964,0.0002207221,0.02579462,0.004120586,0.0001956405,0.002056048,0.08676982],"study_design_scores_gemma":[0.000019511,0.0005485284,0.7068833,0.00002566343,0.0000857659,0.00008888746,0.0003928355,0.2866457,0.003159105,0.0007311546,0.001381649,0.00003789798],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9853166,0.00009474855,0.01157125,0.0001484545,0.00002332864,0.00005449826,0.001964812,0.000129836,0.0006964361],"genre_scores_gemma":[0.9904662,0.0000908705,0.005769082,0.0000275271,0.00001973473,0.00006620616,0.003050862,0.000007106175,0.0005024032],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005334788,"threshold_uncertainty_score":0.01060748,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2632827549902834,"score_gpt":0.5117098996497766,"score_spread":0.2484271446594933,"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."}}