{"id":"W2790815173","doi":"10.1109/ccwc.2018.8301675","title":"The effects of neighbourhood characteristics on crime incidence","year":2018,"lang":"en","type":"article","venue":"","topic":"Crime Patterns and Interventions","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"MacEwan University","funders":"","keywords":"Neighbourhood (mathematics); Law enforcement; Geography; Crime analysis; Criminology; Computer science; Sociology; Political science; Mathematics; Law","routes":{"ca_aff":true,"ca_fund":false,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002369005,0.00003642431,0.00005405953,0.00001516589,0.0003965765,0.00004289174,0.0001874745,0.00002372784,0.001264181],"category_scores_gemma":[0.0005563313,0.0000232209,0.00005080865,0.00006820421,0.0002753516,0.00004656017,0.00002801145,0.00004031492,0.0001846026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001629376,"about_ca_system_score_gemma":0.00002551457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001483637,"about_ca_topic_score_gemma":0.0008705253,"domain_scores_codex":[0.9994742,0.00006921887,0.0001094478,0.00006792539,0.0001532212,0.0001259469],"domain_scores_gemma":[0.9993859,0.0002967171,0.00005514883,0.0001305473,0.00009643169,0.00003526502],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00003222836,0.0003018944,0.02706162,0.00004613025,0.00005474396,0.000003048924,0.01007864,1.369187e-8,0.005051771,0.7843068,0.03071565,0.1423475],"study_design_scores_gemma":[0.0002304784,0.001189082,0.844748,0.0002359233,0.00003584713,4.73173e-7,0.001635993,0.0000328269,0.02287167,0.003297663,0.1255439,0.0001781277],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6772005,0.00003374484,0.0004623955,0.0008364774,0.0010619,0.0001005946,0.000002385617,0.00002917275,0.3202728],"genre_scores_gemma":[0.9909191,0.00003149169,0.00001982369,0.000175302,0.0002683747,0.000004116482,2.891588e-7,0.000002765918,0.008578766],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8176864,"threshold_uncertainty_score":0.9996488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01950579092416243,"score_gpt":0.350473959404681,"score_spread":0.3309681684805186,"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."}}