{"id":"W2057135357","doi":"10.1057/palgrave.sj.8340159","title":"Computer Simulation as a Tool for Environmental Criminologists","year":2004,"lang":"en","type":"article","venue":"Security Journal","topic":"Wildlife Conservation and Criminology Analyses","field":"Environmental Science","cited_by":78,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Situational ethics; Airport security; Crime prevention; Field (mathematics); Computer science; Intervention (counseling); Macro; Computer security; Environmental crime; Organised crime; Criminology; Management science; Data science; Sociology; Psychology; Engineering; Social psychology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001706424,0.0001023209,0.0001021658,0.00003273211,0.0002988751,0.00004427744,0.000154812,0.00008716544,0.001671797],"category_scores_gemma":[0.00004263673,0.00009325203,0.0001019027,0.00003878903,0.0001685573,0.0002550885,0.00006181284,0.0001767641,0.0004585502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001720186,"about_ca_system_score_gemma":0.00001750214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001652689,"about_ca_topic_score_gemma":0.00001341278,"domain_scores_codex":[0.9992,0.00003585374,0.0002098437,0.0001693983,0.0001734918,0.0002113855],"domain_scores_gemma":[0.9996551,0.00005267528,0.0001027325,0.0001073908,0.000006086216,0.00007599843],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0005574921,0.001641855,0.3246743,0.00002937689,0.0002580072,0.0002076423,0.00787789,0.5205297,0.006200159,0.006477543,0.01632688,0.1152192],"study_design_scores_gemma":[0.007264456,0.001343329,0.6276704,0.00004558606,0.0002458986,0.001313171,0.0007127362,0.03670655,0.003473127,0.1961404,0.123959,0.001125331],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.963304,0.00003139388,0.03428422,0.001559781,0.0001731743,0.0001260011,0.000004725687,0.00002480905,0.0004918326],"genre_scores_gemma":[0.9934787,0.00001795435,0.003520042,0.002694612,0.000143695,0.000004692326,0.000008691889,0.000006865528,0.0001247827],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4838232,"threshold_uncertainty_score":0.9992408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03354953789654003,"score_gpt":0.2892818516953672,"score_spread":0.2557323137988272,"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."}}