{"id":"W2157631873","doi":"10.1186/1476-072x-5-46","title":"Identifying geographic areas with high disease rates: when do confidence intervals for rates and a disease cluster detection method agree?","year":2006,"lang":"en","type":"article","venue":"International Journal of Health Geographics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Fondation pour la Recherche Médicale","keywords":"Confidence interval; Cluster (spacecraft); Spatial epidemiology; Statistics; Disease surveillance; Population; Computer science; Disease; Epidemiology; Medicine; Environmental health; Mathematics; Pathology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.3731585,0.001194221,0.003518279,0.005693601,0.002432405,0.008057223,0.007542931,0.007685564,0.002910805],"category_scores_gemma":[0.8148112,0.001403119,0.005104541,0.008747336,0.006908694,0.01036747,0.005814828,0.006553791,0.0009869772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002839099,"about_ca_system_score_gemma":0.003005423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005155046,"about_ca_topic_score_gemma":0.002705671,"domain_scores_codex":[0.5991622,0.2725461,0.0372451,0.03554502,0.04968521,0.005816318],"domain_scores_gemma":[0.08195728,0.8374731,0.0310278,0.02482227,0.02290623,0.001813328],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.007652594,0.0004981369,0.4981767,0.004576562,0.008449963,0.001294019,0.0103686,0.03876081,0.001897209,0.07302513,0.03983577,0.3154645],"study_design_scores_gemma":[0.002582739,0.003037337,0.3339629,0.009223924,0.004495773,0.003402055,0.01108182,0.1804408,0.01280713,0.3756772,0.06207423,0.001213971],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.250812,0.01957046,0.6668018,0.02913598,0.002795893,0.003018288,0.005873252,0.001591341,0.0204009],"genre_scores_gemma":[0.8002101,0.001436857,0.1867115,0.004611986,0.0008463687,0.001962468,0.002921548,0.0007189188,0.0005802432],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3731585,"threshold_uncertainty_score":0.7730072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02259605100855748,"score_gpt":0.3560781762782666,"score_spread":0.3334821252697091,"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."}}