{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02085705,0.0004836397,0.0007368827,0.008277205,0.0008933333,0.002986231,0.001411364,0.000540028,0.002256043],"category_scores_gemma":[0.09072358,0.000240806,0.0006861286,0.008799768,0.0006159087,0.003038528,0.001633556,0.0007905207,0.0009087498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00423677,"about_ca_system_score_gemma":0.009994854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1205774,"about_ca_topic_score_gemma":0.2459376,"domain_scores_codex":[0.9833404,0.005070537,0.001598054,0.0007941388,0.008638005,0.0005587784],"domain_scores_gemma":[0.8827296,0.04176224,0.0151412,0.003747215,0.05346601,0.003153752],"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":[0.0003574588,0.00007112447,0.2020066,0.008904306,0.0003100193,0.0001350905,0.00610854,0.0004444979,0.001296491,0.000654571,0.035725,0.7439862],"study_design_scores_gemma":[0.00006161291,0.0005805511,0.7922415,0.01034658,0.0007062622,0.00055896,0.009295415,0.00436035,0.002229105,0.0006546803,0.178704,0.0002610275],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6005849,0.1405362,0.07131646,0.02236849,0.001923766,0.008153477,0.0414915,0.003731067,0.1098941],"genre_scores_gemma":[0.8651488,0.04953353,0.05955633,0.002035262,0.0006935659,0.002662337,0.01356086,0.0002306997,0.006578513],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1205774,"threshold_uncertainty_score":0.2397511,"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."}}