{"id":"W2883020963","doi":"10.2196/11203","title":"Bringing Real-Time Geospatial Precision to HIV Surveillance Through Smartphones: Feasibility Study","year":2018,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Geospatial analysis; Mobile phone; Public health; Phone; Computer science; Human immunodeficiency virus (HIV); Environmental health; Population; Public health surveillance; Medicine; Geography; Remote sensing; Telecommunications; Virology","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":[0.004991594,0.0005813318,0.0002532708,0.0007543647,0.000529803,0.000848603,0.0005602385,0.0008630004,0.003221734],"category_scores_gemma":[0.008662885,0.0003369485,0.0006405553,0.0003276586,0.0006501349,0.001541751,0.001198449,0.0006313854,0.0006967418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005771291,"about_ca_system_score_gemma":0.001668684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004183871,"about_ca_topic_score_gemma":0.004758121,"domain_scores_codex":[0.9966862,0.00204015,0.0001694437,0.0001939368,0.0004252297,0.000485124],"domain_scores_gemma":[0.9947396,0.002331049,0.0004796386,0.0003001278,0.001345135,0.0008044801],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.005190565,0.0402646,0.7874174,0.001648276,0.0002536126,0.003433636,0.008022141,0.001205617,0.01185394,0.000905134,0.003071052,0.1367342],"study_design_scores_gemma":[0.002396471,0.1636494,0.7763014,0.0004183271,0.0005819276,0.003445219,0.02763596,0.01067406,0.005285984,0.0007715043,0.008670452,0.0001692599],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9934469,0.00008982697,0.001997448,0.0002929055,0.00002054152,0.002433557,0.0001687877,0.00002009258,0.001529978],"genre_scores_gemma":[0.9907693,0.0002069394,0.006120964,0.0002332834,0.00003322657,0.002002477,0.0001903236,0.000004542658,0.0004390331],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004991594,"threshold_uncertainty_score":0.02639836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06426486434608804,"score_gpt":0.4267490583931758,"score_spread":0.3624841940470878,"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."}}