{"id":"W4389010731","doi":"10.1007/978-981-99-8145-8_15","title":"Traffic Accident Forecasting Based on a GrDBN-GPR Model with Integrated Road Features","year":2023,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Traffic and Road Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Support vector machine; Benchmark (surveying); Feature (linguistics); Kriging; Artificial intelligence; Key (lock); Machine learning; Data mining; Ground-penetrating radar; Stability (learning theory); Gaussian process; Gaussian; Radar; Geography; Computer security","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.0003075005,0.0006034502,0.000864133,0.0004938431,0.0002835317,0.0006567649,0.001287463,0.001137136,0.001620503],"category_scores_gemma":[0.0007744296,0.0004226732,0.00082791,0.0009997616,0.000302477,0.001064522,0.0003129722,0.0008571943,0.0005392472],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007053525,"about_ca_system_score_gemma":0.0007086557,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05417828,"about_ca_topic_score_gemma":0.02463516,"domain_scores_codex":[0.9998446,0.00002624208,0.000007783317,0.0000578082,0.00002922968,0.00003446635],"domain_scores_gemma":[0.9997761,0.0000818286,0.00002372277,0.00001874574,0.00008285572,0.00001686318],"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":[0.00003171774,0.00002517385,0.001076911,0.00001178365,0.00001506144,0.00003006813,0.000006726891,0.991217,0.0003088162,0.0003439489,0.0004172482,0.006515635],"study_design_scores_gemma":[0.00000192669,0.000004279541,0.0002387431,8.867004e-7,0.000004782585,0.000003396957,0.000001989219,0.9995442,0.0000377754,0.0001203114,0.00003937014,0.000002307175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7097529,0.00090353,0.2662821,0.0008449097,0.0005168175,0.00006707812,0.002269111,0.001973572,0.01739006],"genre_scores_gemma":[0.98823,0.0002037478,0.007997188,0.00003912931,0.00004518517,0.00002192361,0.0005925614,0.00003481176,0.00283549],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05417828,"threshold_uncertainty_score":0.1077259,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03953975423910483,"score_gpt":0.2453669771569404,"score_spread":0.2058272229178356,"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."}}