{"id":"W3028588294","doi":"10.2139/ssrn.3573193","title":"Mobile Health Behavior Tracking: Health Effects of Tracking Consistency and Its Prediction","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Consumer Market Behavior and Pricing","field":"Business, Management and Accounting","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Tracking (education); Consistency (knowledge bases); Computer science; Artificial intelligence; Econometrics; Psychology; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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":[0.00128013,0.0001849738,0.0003749502,0.0001675058,0.0003777306,0.0001231372,0.0001469845,0.00005300943,0.00002240833],"category_scores_gemma":[0.0001043324,0.0001805674,0.0001044053,0.0003342006,0.00003016277,0.0006948455,0.00005463369,0.0009909072,0.000005333114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001772917,"about_ca_system_score_gemma":0.0007882345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001658527,"about_ca_topic_score_gemma":0.0001699228,"domain_scores_codex":[0.997685,0.00005660056,0.0005379199,0.0002498857,0.0002719867,0.001198628],"domain_scores_gemma":[0.9991092,0.00004815925,0.0005651796,0.00008797018,0.0001278918,0.0000616043],"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":[0.000102459,0.0002355869,0.1177781,0.001197237,0.0000956821,0.00001233911,0.0009532946,0.000009871165,0.002778611,0.009501158,0.0001780242,0.8671576],"study_design_scores_gemma":[0.02358861,0.01303864,0.8484918,0.003965116,0.003138995,0.002258437,0.02997773,0.00887297,0.002184761,0.01896491,0.04158248,0.003935535],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9688853,0.02736892,0.0009477701,0.001287587,0.0003544011,0.0008762694,0.000003522869,0.00009619818,0.0001800239],"genre_scores_gemma":[0.9948823,0.003756439,0.00001688393,0.0008115296,0.0004426461,0.0000245669,0.000007558096,0.0000306783,0.00002742212],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8632221,"threshold_uncertainty_score":0.7363327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02011916914316668,"score_gpt":0.2618781147487387,"score_spread":0.241758945605572,"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."}}