{"id":"W2581468326","doi":"10.5210/ojphi.v8i3.6937","title":"KIWI: A technology for public health event monitoring and early warning detection","year":2016,"lang":"en","type":"article","venue":"Online Journal of Public Health Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Food Inspection Agency; Public Health Agency of Canada","funders":"Canadian Food Inspection Agency","keywords":"Warning system; Public health surveillance; Public health; Event monitoring; Event (particle physics); Disease surveillance; Computer science; Data collection; Data science; Medicine; Environmental health; Telecommunications; Wireless sensor network; Pathology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.003195121,0.001388704,0.0006168625,0.005398071,0.001256566,0.002896826,0.00234493,0.001032644,0.005354728],"category_scores_gemma":[0.00856154,0.0006206257,0.0005133406,0.003095027,0.0008522609,0.003871656,0.002824318,0.001147203,0.002408992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001983601,"about_ca_system_score_gemma":0.00507017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1183589,"about_ca_topic_score_gemma":0.1003569,"domain_scores_codex":[0.9979256,0.0002898674,0.0001346744,0.0003051509,0.001161925,0.0001827414],"domain_scores_gemma":[0.9947694,0.001487273,0.0004848668,0.0007908134,0.001925093,0.0005424275],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001059876,0.0005238688,0.01865184,0.001332902,0.0002566719,0.0009178187,0.003138675,0.004840637,0.03523196,0.01134765,0.09683196,0.8258661],"study_design_scores_gemma":[0.0005254365,0.0009636903,0.07676222,0.001307319,0.0005605433,0.002230929,0.003400096,0.2134541,0.06693823,0.02447479,0.6083809,0.001001832],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04441539,0.001988048,0.7302027,0.003471442,0.000442923,0.003949347,0.01188725,0.1587292,0.04491364],"genre_scores_gemma":[0.2465006,0.001901165,0.7145807,0.001271856,0.0002136157,0.001416065,0.0138885,0.001712919,0.01851459],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1183589,"threshold_uncertainty_score":0.2353399,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07662715370780948,"score_gpt":0.3710911508023295,"score_spread":0.29446399709452,"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."}}