{"id":"W2904290903","doi":"10.48550/arxiv.1812.07410","title":"An Improved Deep Belief Network Model for Road Safety Analyses","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002401097,0.0002795068,0.0002986928,0.0001444392,0.000450685,0.0001311455,0.001980289,0.0003197882,0.00001229703],"category_scores_gemma":[0.000008377116,0.0003184327,0.0002864541,0.000517405,0.0001013109,0.000340503,0.0009314643,0.0002745626,0.0000142232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001504269,"about_ca_system_score_gemma":0.0001505366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001165005,"about_ca_topic_score_gemma":0.0001091602,"domain_scores_codex":[0.9981107,0.00005316627,0.0002400417,0.001176001,0.00005375762,0.0003663246],"domain_scores_gemma":[0.9974655,0.00003793379,0.0002975755,0.001726165,0.0002915322,0.0001813198],"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":[0.00003474335,0.00009072544,0.00006524619,0.0000198796,0.00007150822,0.00000246144,0.00007365552,0.917986,0.0001224026,0.07660648,0.0003776718,0.004549201],"study_design_scores_gemma":[0.0001592386,0.00009521266,0.0001310345,0.00001292842,0.00006532478,8.581466e-7,0.000007609525,0.900614,0.0002012991,0.0979148,0.0004684201,0.0003293142],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006837083,0.00002591273,0.9908739,0.00005286509,0.0001490234,0.0006008804,0.00002899514,0.0006484824,0.0007829095],"genre_scores_gemma":[0.8839686,0.00009079361,0.114581,0.0001407854,0.0002120399,0.00001345343,0.00003646352,0.00002049272,0.0009364001],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8771315,"threshold_uncertainty_score":0.9999267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1007032664353296,"score_gpt":0.2499663142212201,"score_spread":0.1492630477858905,"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."}}