{"id":"W2890238901","doi":"10.5210/ojphi.v10i2.8547","title":"User rating activity within KIWI: A technology for public health event monitoring and early warning signal detection","year":2018,"lang":"en","type":"article","venue":"Online Journal of Public Health Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Agency of Canada; Canadian Women's Health Network","funders":"Canadian Food Inspection Agency","keywords":"Reliability (semiconductor); Rating scale; Applied psychology; Confidence interval; Computer science; Medicine; Psychology; Statistics; Mathematics","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":[0.006529269,0.0002080899,0.0007156995,0.0008441132,0.0004150326,0.0001257745,0.0001877706,0.0001376878,0.000006872014],"category_scores_gemma":[0.002623213,0.0001778628,0.0000962437,0.0006528489,0.0001314209,0.001298432,0.0001026126,0.0007741845,0.000002289356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006472532,"about_ca_system_score_gemma":0.003197227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003030781,"about_ca_topic_score_gemma":0.00004465267,"domain_scores_codex":[0.9963976,0.0001846228,0.001976899,0.0001281701,0.0005702004,0.0007424857],"domain_scores_gemma":[0.994341,0.0001521611,0.003064956,0.0002629982,0.001228355,0.000950533],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001625731,0.0004338988,0.08794935,0.001531803,0.00016894,0.000004204074,0.005792694,0.00002244,0.0004035995,0.0000791656,0.0003897611,0.9030616],"study_design_scores_gemma":[0.02153591,0.03657916,0.5073863,0.004902922,0.0001476426,0.003708622,0.02917583,0.1202957,0.002144947,0.0003456549,0.2724002,0.001377106],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8167779,0.0002681813,0.1668607,0.01500255,0.000442646,0.0005091641,0.00005008419,0.00007829155,0.00001042478],"genre_scores_gemma":[0.9280512,0.0001447908,0.07020221,0.0006426917,0.0008858182,0.00001070162,0.00001581069,0.00002775257,0.00001901063],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9016845,"threshold_uncertainty_score":0.7253034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0746915077265956,"score_gpt":0.376613282896167,"score_spread":0.3019217751695714,"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."}}