{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004458218,0.0001735823,0.0006558778,0.0009464711,0.0001954283,0.00006178601,0.0001852888,0.0001153922,0.000006363583],"category_scores_gemma":[0.003166533,0.0001192042,0.0001041685,0.0004701307,0.00007978859,0.0009573797,0.00008051866,0.0003926752,0.000003617281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007082499,"about_ca_system_score_gemma":0.002507535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009976314,"about_ca_topic_score_gemma":0.00001088041,"domain_scores_codex":[0.9965063,0.0001073272,0.002074069,0.0001016838,0.0004745635,0.0007360175],"domain_scores_gemma":[0.9954374,0.0001640583,0.002187214,0.0002815472,0.0008642421,0.001065472],"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":[0.00004021924,0.0001722023,0.0532123,0.0007294103,0.00008448104,0.000002479771,0.0008280595,0.000001347751,0.00006331578,0.00008912036,0.0004654704,0.9443116],"study_design_scores_gemma":[0.009883267,0.007380405,0.2191397,0.002259574,0.00003655997,0.001507892,0.004880174,0.001651245,0.0001031377,0.0002797096,0.7524997,0.0003786136],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7296181,0.001350277,0.171163,0.09646943,0.0005690084,0.0005891963,0.0001221353,0.0001030898,0.0000157567],"genre_scores_gemma":[0.9519653,0.002147838,0.04423325,0.0009366948,0.0006014412,0.00001474319,0.00001758644,0.00003110912,0.00005207423],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.943933,"threshold_uncertainty_score":0.4861008,"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."}}