{"id":"W1999168261","doi":"10.3138/cjccj.2012.e13","title":"Exploring Hotspots of Drug Offences in Toronto: A Comparison of Four Local Spatial Cluster Detection Methods","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Criminology and Criminal Justice/La Revue canadienne de criminologie et de justice pénale","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Scan statistic; Statistic; Contiguity; Spatial analysis; Geography; Cluster (spacecraft); Euclidean distance; Downtown; Cartography; Statistics; Computer science; Mathematics; Artificial intelligence; Remote sensing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001712262,0.000361534,0.001222707,0.0007200798,0.0001178617,0.0000305342,0.0004567388,0.0003352967,0.0002033422],"category_scores_gemma":[0.004288925,0.0003672401,0.000190504,0.0001485131,0.0009341433,0.0005349386,0.00006726082,0.0009216816,0.00000295772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001246919,"about_ca_system_score_gemma":0.002360877,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1729668,"about_ca_topic_score_gemma":0.4313237,"domain_scores_codex":[0.9961705,0.0008825132,0.001275003,0.0004004691,0.0001501205,0.001121458],"domain_scores_gemma":[0.9952863,0.001478226,0.0008023938,0.0004044438,0.0007451471,0.00128351],"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":[0.003140736,0.0006038483,0.05514966,0.01882295,0.0007000179,0.004385527,0.083384,0.002891031,0.02145907,0.005378557,0.0007435599,0.803341],"study_design_scores_gemma":[0.002274936,0.001683229,0.7146584,0.001139758,0.007390094,0.004747144,0.2543498,0.005088932,0.007105683,0.000678667,0.0003752233,0.0005081047],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9812459,0.00268314,0.01366855,0.0006557925,0.0006137535,0.0003142508,0.00003432402,0.00001536211,0.0007689086],"genre_scores_gemma":[0.9879156,0.0005803777,0.01083264,0.0003809595,0.0001839158,0.00003507765,0.000007991055,0.00003544109,0.00002796886],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8028329,"threshold_uncertainty_score":0.9998779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1756424763747665,"score_gpt":0.3672800152606365,"score_spread":0.19163753888587,"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."}}