{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002225211,0.0001208752,0.0001899367,0.0005049151,0.00008194314,0.0000342578,0.00005654792,0.00008666365,0.00003180415],"category_scores_gemma":[0.0002357806,0.00011633,0.00008962626,0.002304667,0.00004157344,0.00007644967,0.0000485438,0.00008441751,0.00005610032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004708263,"about_ca_system_score_gemma":0.000049303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001204956,"about_ca_topic_score_gemma":0.00008558555,"domain_scores_codex":[0.9988377,0.00004548263,0.000260616,0.000235334,0.0003933428,0.0002275015],"domain_scores_gemma":[0.9993265,0.00005266448,0.0001175903,0.0002522782,0.0001503895,0.000100526],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001653332,0.00284689,0.5710337,0.0006195179,0.0002256505,0.0002954176,0.00003811092,0.0004354746,0.1815961,0.0001438781,0.01322921,0.2278827],"study_design_scores_gemma":[0.001965534,0.0001847311,0.9067222,0.00004427635,0.0001162545,0.00008778684,0.00000819946,0.02742317,0.006991018,0.00005577343,0.05617769,0.0002233251],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8203954,0.0002764533,0.1779058,0.0000847843,0.0004162753,0.0002695139,0.0004012028,0.0001049975,0.0001456193],"genre_scores_gemma":[0.9974105,0.000007717052,0.0005775288,0.00005987161,0.000424062,0.000004467009,0.001411986,0.00001687423,0.00008698199],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3356885,"threshold_uncertainty_score":0.4743799,"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."}}