{"id":"W2772790809","doi":"10.1177/2399808317744779","title":"Time-varying relationships between land use and crime: A spatio-temporal analysis of small-area seasonal property crime trends","year":2017,"lang":"en","type":"article","venue":"Environment and Planning B Urban Analytics and City Science","topic":"Crime Patterns and Interventions","field":"Social Sciences","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Property crime; Land use; Spatial ecology; Bayesian probability; Scale (ratio); Property (philosophy); Geography; Temporal scales; Regression analysis; Seasonality; Econometrics; Physical geography; Environmental science; Ecology; Cartography; Statistics; Mathematics; Violent crime; Psychology; Criminology","routes":{"ca_aff":true,"ca_fund":true,"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":["sts"],"consensus_categories":[],"category_scores_codex":[0.001076796,0.000106704,0.0002217809,0.0002432709,0.001993281,0.0005306415,0.0001946333,0.00005582373,0.0002319796],"category_scores_gemma":[0.0002047543,0.00008470644,0.00006247452,0.0002042112,0.001433661,0.000491,0.0001415666,0.0001265767,9.778486e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002863129,"about_ca_system_score_gemma":0.00003011939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002148999,"about_ca_topic_score_gemma":0.0002183995,"domain_scores_codex":[0.9988961,0.00006028565,0.0001993725,0.0003207098,0.0003117468,0.0002117481],"domain_scores_gemma":[0.9992537,0.0001140379,0.0001969434,0.0002096048,0.00002925896,0.0001964072],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000005187635,0.0000196593,0.9965044,0.000003711327,0.00007273519,0.000001353143,0.002389288,0.00004270613,0.00002515978,0.0001000531,0.0001669983,0.0006687203],"study_design_scores_gemma":[0.0001229751,0.00003810304,0.9755874,0.00003452961,0.0003770448,3.055257e-7,0.0002866648,0.02178131,0.00001282451,0.0000412148,0.00159257,0.000125038],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965599,0.0001375959,0.0004613317,0.0002557749,0.00001971977,0.00005845895,0.00006097332,0.000008186188,0.002438126],"genre_scores_gemma":[0.9956985,0.0000531612,0.0003315803,0.00001143743,0.00003832494,0.00000133979,0.00002976266,0.00000332917,0.003832627],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0217386,"threshold_uncertainty_score":0.999306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1620364172779291,"score_gpt":0.3231014570522263,"score_spread":0.1610650397742971,"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."}}