{"id":"W4283580437","doi":"10.22214/ijraset.2022.44577","title":"Deep Learning Process in Analyzing Crimes","year":2022,"lang":"en","type":"article","venue":"International Journal for Research in Applied Science and Engineering Technology","topic":"Crime Patterns and Interventions","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Decision tree; Computer science; Crime analysis; Artificial intelligence; Process (computing); Machine learning; Criminology; Data science; Psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005628488,0.00004616084,0.0000761767,0.002727535,0.0008142426,0.0001728087,0.000837522,0.00003261022,0.00007040791],"category_scores_gemma":[0.0007236089,0.0000506679,0.00001959331,0.001613362,0.0003249746,0.0001577388,0.0002491257,0.0008436836,0.000001115596],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005752445,"about_ca_system_score_gemma":0.0001852212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001092294,"about_ca_topic_score_gemma":0.0001046636,"domain_scores_codex":[0.998387,0.00002806591,0.0001835731,0.0001855632,0.0007813894,0.0004344539],"domain_scores_gemma":[0.9995081,0.00009996975,0.00003449224,0.00004813749,0.0002506321,0.00005865431],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001189858,0.0003423786,0.04954493,0.00004239217,0.00004093513,0.0001228521,0.01594412,0.1291939,0.02691969,0.5629376,0.0002562931,0.2145359],"study_design_scores_gemma":[0.003285315,0.0007083377,0.01054685,0.0003396296,0.000008043886,0.0002522107,0.1965523,0.3260579,0.004156775,0.2058945,0.2513226,0.0008755794],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9868624,0.0002244047,0.003770912,0.00581251,0.0005092669,0.0002445252,0.000001358331,0.00004981201,0.002524818],"genre_scores_gemma":[0.9992752,0.00008106949,0.000376194,0.00001092412,0.00006155195,0.000136085,5.600511e-7,0.000005364313,0.00005306253],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3570431,"threshold_uncertainty_score":0.6262578,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0831829695976723,"score_gpt":0.4724665732862821,"score_spread":0.3892836036886098,"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."}}