{"id":"W4386233701","doi":"10.1109/istas55053.2022.10227088","title":"How Mobile Health Technologies Can Transform Social Relationships in a Population-Level Fitness Promotion Campaign","year":2022,"lang":"en","type":"article","venue":"","topic":"Innovative Human-Technology Interaction","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Promotion (chess); Computer science; Health promotion; Population; Medicine; Political science; Environmental health; Public health","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003800836,0.0005368298,0.0002537915,0.001637818,0.005081888,0.008204954,0.001026139,0.002772543,0.01830847],"category_scores_gemma":[0.01046072,0.0003616442,0.0006589391,0.0006660111,0.003763908,0.006542713,0.007516356,0.002400416,0.003390756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001431691,"about_ca_system_score_gemma":0.00207221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002968073,"about_ca_topic_score_gemma":0.004636,"domain_scores_codex":[0.9964413,0.002543864,0.00005946976,0.0002592001,0.0002661411,0.0004299036],"domain_scores_gemma":[0.9958131,0.002295921,0.0002844628,0.0001839854,0.000246214,0.001176324],"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":[0.0002567651,0.003765968,0.03591653,0.001394086,0.000188797,0.00265756,0.2812164,0.0005759049,0.003164135,0.1014761,0.08721092,0.4821768],"study_design_scores_gemma":[0.0003147057,0.001428241,0.03483653,0.002271064,0.0002703131,0.0008609717,0.1747982,0.00169493,0.001165634,0.06329592,0.7189345,0.000128969],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4050911,0.006484029,0.01321956,0.1695113,0.003729579,0.0007742953,0.0003699512,0.000607523,0.4002127],"genre_scores_gemma":[0.9430298,0.004147738,0.006126496,0.01637194,0.0008949179,0.0006140922,0.0001743946,0.0001418124,0.02849882],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01830847,"threshold_uncertainty_score":0.06124789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08245367847522853,"score_gpt":0.3076103361814482,"score_spread":0.2251566577062197,"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."}}