{"id":"W2023771054","doi":"10.1111/j.1541-0420.2005.00503.x","title":"Spatial Event Cluster Detection Using a Compound Poisson Distribution","year":2006,"lang":"en","type":"article","venue":"Biometrics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Heritage Foundation for Medical Research","keywords":"Poisson distribution; Geography; Cluster (spacecraft); Event (particle physics); Population; Poisson regression; Distribution (mathematics); Cartography; Disease surveillance; Computer science; Statistics; Disease; Medicine; Environmental health; 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":[0.00775735,0.0004581882,0.0009448666,0.002655362,0.0007336648,0.001463205,0.002135696,0.0008622918,0.001453822],"category_scores_gemma":[0.0226957,0.0004477132,0.001203525,0.002277548,0.0007026918,0.001199187,0.001771119,0.0009097033,0.0003176572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001195957,"about_ca_system_score_gemma":0.00180926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01074944,"about_ca_topic_score_gemma":0.007210785,"domain_scores_codex":[0.9958698,0.001707198,0.0003000954,0.001048885,0.000867888,0.0002060638],"domain_scores_gemma":[0.985891,0.009702442,0.001381368,0.001184303,0.001622542,0.0002182111],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005379729,0.000285429,0.2250107,0.0003799006,0.0006733889,0.001061041,0.0009518122,0.3199449,0.004423681,0.06536528,0.01025507,0.3711107],"study_design_scores_gemma":[0.00002604703,0.00003961236,0.006376129,0.00001511369,0.00003040114,0.0002233438,0.0000894101,0.9768446,0.00126194,0.01329629,0.00177046,0.0000266604],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03826125,0.0000861109,0.9594867,0.0002415397,0.00003215429,0.0002303821,0.000489501,0.000623912,0.0005484034],"genre_scores_gemma":[0.5652907,0.0001560207,0.4312119,0.0001218242,0.00006666737,0.0004385365,0.001293405,0.00006394668,0.001357062],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01074944,"threshold_uncertainty_score":0.04102528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01955395325706992,"score_gpt":0.2865288886447513,"score_spread":0.2669749353876814,"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."}}