{"id":"W4405191211","doi":"10.3390/urbansci8040247","title":"Analyzing Urban Crime Through Street View Imagery: Insights from Urban Micro Built Environment and Perceptions","year":2024,"lang":"en","type":"article","venue":"Urban Science","topic":"Crime Patterns and Interventions","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"University of Toronto","keywords":"Fear of crime; Urbanization; Perception; Built environment; Crime prevention; Context (archaeology); Urban planning; Geography; Criminology; Transport engineering; Psychology; Engineering; Civil engineering; Economic growth","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":[0.0001830405,0.0002125916,0.0001217392,0.001276428,0.0002622246,0.0008426523,0.0001408393,0.0001251239,0.001236795],"category_scores_gemma":[0.00135554,0.0001015302,0.0001423401,0.00141172,0.0005439814,0.0004131008,0.000611243,0.0002096043,0.0001264928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005344687,"about_ca_system_score_gemma":0.0003414016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07910982,"about_ca_topic_score_gemma":0.1963272,"domain_scores_codex":[0.9998492,0.00003772802,0.000006167988,0.00001933978,0.00005531198,0.00003215915],"domain_scores_gemma":[0.9994422,0.0001418073,0.0001791061,0.00003942416,0.0001327388,0.00006478719],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008590236,0.00006510356,0.9487956,0.0001186194,0.00005869132,0.0002442218,0.01516249,0.002151076,0.001920979,0.001297873,0.001220765,0.02887885],"study_design_scores_gemma":[9.194131e-7,0.00001778219,0.9798592,0.00002830454,0.00001215905,0.00006797507,0.01511798,0.003289377,0.0003029101,0.0002144013,0.001081336,0.000007654062],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996154,0.00005536141,0.0006924312,0.00006538414,0.000001912251,0.00001108943,0.0005200971,0.000007477598,0.002492185],"genre_scores_gemma":[0.9989198,0.00005910512,0.0005607735,0.000005864373,0.000001564354,0.000004651856,0.0002571001,0.000003211573,0.0001879629],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07910982,"threshold_uncertainty_score":0.1572987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03910559796113851,"score_gpt":0.3351354459611835,"score_spread":0.296029848000045,"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."}}