{"id":"W4391423602","doi":"10.1109/ieem58616.2023.10406631","title":"Traffic Collision Detection Using DenseNet","year":2023,"lang":"en","type":"article","venue":"","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Collision; Graphical user interface; Traffic accident; Real-time computing; Computer security; Artificial intelligence; Transport engineering; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003248707,0.0009328781,0.0004800782,0.001772572,0.0004622079,0.0007506397,0.0009796303,0.0006594845,0.00177271],"category_scores_gemma":[0.001040439,0.0004650346,0.0004672779,0.0009198331,0.0002748839,0.0007617573,0.000857821,0.0005893349,0.0004857849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009966566,"about_ca_system_score_gemma":0.001190355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03206749,"about_ca_topic_score_gemma":0.03444086,"domain_scores_codex":[0.9997897,0.00001964209,0.00001017709,0.00007050751,0.00005943236,0.00005055686],"domain_scores_gemma":[0.9997538,0.00004594174,0.00003993246,0.00002897409,0.0001038467,0.00002753066],"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":[0.0005802506,0.0006522987,0.03105214,0.0001648749,0.0002640242,0.0006675221,0.0001878214,0.5066419,0.01098604,0.004888786,0.0213657,0.4225487],"study_design_scores_gemma":[0.00001025787,0.00004269796,0.002350501,0.000009289576,0.00001545242,0.00005712397,0.00003180805,0.992357,0.001746609,0.002045726,0.00132486,0.000008658119],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4706433,0.0008792692,0.5071527,0.0006373636,0.0003055999,0.0002522214,0.002984449,0.008984121,0.008160921],"genre_scores_gemma":[0.9325076,0.0002711696,0.05910854,0.0001201339,0.00005894013,0.00007092323,0.004139188,0.00008292461,0.003640569],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03206749,"threshold_uncertainty_score":0.06376165,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01737787447783206,"score_gpt":0.2264644842398061,"score_spread":0.209086609761974,"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."}}