{"id":"W101425453","doi":"10.1016/j.amepre.2011.01.008","title":"Mobile Health","year":2011,"lang":"en","type":"article","venue":"American Journal of Preventive Medicine","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":84,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Cancer Institute; University of California, San Diego; National Institutes of Health; U.S. Department of Health and Human Services","keywords":"mHealth; Cyberinfrastructure; Quarter (Canadian coin); Mobile technology; Telecommunications; Computer science; Business; Internet privacy; Environmental health; Mobile computing; Data science; Geography; Medicine; Nursing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002902336,0.00014736,0.0007715353,0.0002570027,0.0003708483,6.58557e-7,0.0002881076,0.00004053455,0.002064996],"category_scores_gemma":[0.0002514368,0.0001038459,0.00008846302,0.0005130852,0.0004278933,0.00008521605,0.00004089039,0.0007937637,0.0001211751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002153759,"about_ca_system_score_gemma":0.001214687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008781276,"about_ca_topic_score_gemma":0.00004506597,"domain_scores_codex":[0.9963165,0.000989305,0.00160329,0.0001726838,0.0003355405,0.0005826722],"domain_scores_gemma":[0.9952781,0.0003326295,0.002769584,0.0002933155,0.0005013641,0.0008250025],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003128723,0.0002765422,0.02922248,0.0002463757,0.00007374999,0.00001149416,0.02466791,4.434052e-7,0.0000580136,0.006008746,0.06447892,0.8746424],"study_design_scores_gemma":[0.002884103,0.01931468,0.06747074,0.001470334,0.00008097021,0.00006933632,0.0423357,0.000005731485,0.00002006342,0.005206185,0.860954,0.0001880986],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5211045,0.03606009,0.1639491,0.02680654,0.01196122,0.0265465,0.00009632421,0.0003886491,0.2130871],"genre_scores_gemma":[0.974168,0.004394579,0.007900862,0.009040319,0.00122314,0.001430197,0.00000616837,0.00003619646,0.001800524],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8744544,"threshold_uncertainty_score":0.9988472,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06580173288948336,"score_gpt":0.4613607952757763,"score_spread":0.3955590623862929,"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."}}