{"id":"W2978592243","doi":"10.2196/15237","title":"Outcomes of Mobile Reporting to Enhance Disease Surveillance in 632 Districts of 29 States in Nigeria","year":2019,"lang":"en","type":"article","venue":"Iproceedings","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Disease surveillance; Mobile phone; Health care; Population; Medicine; Phone; Medical emergency; Public health; Public health surveillance; Environmental health; Business; Computer science; Nursing; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007713582,0.000197361,0.0009125435,0.0002557366,0.00001062086,0.000009587894,0.0001891677,0.00004989388,0.00007782187],"category_scores_gemma":[0.002747959,0.0001826826,0.00009853896,0.0008134087,0.00005449409,0.0001610688,0.0001235257,0.0001380803,0.00002617821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009779753,"about_ca_system_score_gemma":0.0001293458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001945838,"about_ca_topic_score_gemma":0.0001008197,"domain_scores_codex":[0.9973055,0.00001710948,0.001412425,0.00048307,0.0004228432,0.000359094],"domain_scores_gemma":[0.9981002,0.00013501,0.0009068539,0.0003889658,0.0002480636,0.0002209577],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003814436,0.0002276104,0.982479,0.0007579643,0.00001794821,0.00003865802,0.0008117593,0.00006426753,0.01388487,0.00001543916,0.0003190365,0.00100196],"study_design_scores_gemma":[0.0006804024,0.0001259203,0.9915189,0.0004312257,0.000006405655,0.000001458739,0.000507017,0.0001023524,0.005979697,0.00006239702,0.0004129058,0.0001712951],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977394,0.0002362793,0.000007785805,0.0001625226,0.0001043623,0.0008274599,0.0001694771,0.00004469662,0.0007080715],"genre_scores_gemma":[0.9991323,0.00003670591,0.0002503796,0.0001242658,0.00002326486,0.00006705676,0.00007375651,0.00002841804,0.000263856],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009039878,"threshold_uncertainty_score":0.744958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01014592327839721,"score_gpt":0.3115112846952797,"score_spread":0.3013653614168825,"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."}}