{"id":"W2965161939","doi":"10.5539/cis.v12n3p92","title":"A Predictive Framework of Speed Camera Locations for Road Safety","year":2019,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Classifier (UML); Crash; Enforcement; Process (computing); Random forest; Artificial intelligence; Data mining; Machine learning; Real-time computing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00109947,0.0006284123,0.0005053768,0.001100504,0.00043959,0.0009272793,0.001335073,0.0007566726,0.001381119],"category_scores_gemma":[0.002636049,0.0002340671,0.0005006242,0.0008127483,0.0004608211,0.0008178884,0.0004715514,0.000855568,0.0004235184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00109284,"about_ca_system_score_gemma":0.001160929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02629803,"about_ca_topic_score_gemma":0.01625162,"domain_scores_codex":[0.9995678,0.00009700919,0.00001747905,0.0001433807,0.00009036819,0.00008398368],"domain_scores_gemma":[0.9991817,0.0003566205,0.0001362917,0.00005853904,0.0002226671,0.00004430438],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006608821,0.00008065205,0.007479126,0.00003263349,0.00003182975,0.00008369796,0.00005869123,0.9061127,0.001649625,0.006769995,0.001210137,0.07642484],"study_design_scores_gemma":[0.000001039348,0.00001051556,0.0004772722,0.000003011764,0.000004370688,0.000008164553,0.000005464803,0.9981376,0.0002086295,0.0009512543,0.0001897464,0.000002783126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08702666,0.000460953,0.9082239,0.0004158973,0.00006127191,0.0000606694,0.000298693,0.0009559909,0.002495917],"genre_scores_gemma":[0.9163733,0.0002649359,0.08078524,0.00005507041,0.00007309824,0.00007420894,0.0004578489,0.00004040546,0.001876017],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02629803,"threshold_uncertainty_score":0.05228996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005203368136017381,"score_gpt":0.2205256346889646,"score_spread":0.2153222665529472,"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."}}