{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002820028,0.0003226118,0.0002184317,0.0006473946,0.0006643891,0.0009917883,0.0002971749,0.000310111,0.0007670025],"category_scores_gemma":[0.006652251,0.000204456,0.0002929089,0.0009854378,0.0003629189,0.000487236,0.001066193,0.0003274497,0.0001850852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001270351,"about_ca_system_score_gemma":0.001410919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02039354,"about_ca_topic_score_gemma":0.02990165,"domain_scores_codex":[0.9982288,0.001019144,0.0001586384,0.0001255275,0.0001997382,0.0002681747],"domain_scores_gemma":[0.9960748,0.001337392,0.001346625,0.0001782057,0.0007494551,0.0003135258],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001340633,0.0002818243,0.9758657,0.00009412216,0.00003187507,0.0002598712,0.004678376,0.0003908079,0.0003339824,0.00007131814,0.0003220944,0.01753593],"study_design_scores_gemma":[0.00001322357,0.0008936726,0.9777924,0.00008511441,0.00003881881,0.0001945839,0.01808745,0.00119415,0.0006428116,0.00005851669,0.0009844339,0.00001477524],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988857,0.00006785697,0.0001049786,0.00006283996,0.000004519854,0.00007649604,0.0002064586,0.000006048037,0.0005851857],"genre_scores_gemma":[0.9990066,0.0001150894,0.0003481701,0.00002661131,0.000002630899,0.0000548242,0.0001789512,0.000001683413,0.0002655816],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02039354,"threshold_uncertainty_score":0.0405497,"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."}}