{"id":"W4416759607","doi":"10.1016/j.patcog.2025.112784","title":"Multi-graph spatio-temporal network for traffic accident risk forecasting","year":2025,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Key Research and Development Program of China Stem Cell and Translational Research; National Key Research and Development Program of China; China Scholarship Council; National Natural Science Foundation of China","keywords":"Baseline (sea); Accident (philosophy); Traffic accident; Dual (grammatical number); Spatial database; Sensor fusion; Poison control; Hazard","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002014945,0.0001324886,0.0001203324,0.0001512219,0.0001032352,0.00004787547,0.00007822658,0.00007386142,0.00002019978],"category_scores_gemma":[0.0000210131,0.0001484132,0.00009269368,0.0001660256,0.0000100323,0.0001156172,0.00001699876,0.0001145172,0.00001424926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004285568,"about_ca_system_score_gemma":0.000004316292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001871767,"about_ca_topic_score_gemma":0.0005041522,"domain_scores_codex":[0.9992812,0.00002243592,0.0002423034,0.0001708763,0.00006611882,0.0002170213],"domain_scores_gemma":[0.9997193,0.00005272082,0.00005143425,0.000104027,0.00004035755,0.00003215568],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001059552,0.00002628584,0.00595029,0.0001016259,0.00006803191,9.97444e-7,0.00004836612,0.02060698,0.000007290634,0.000002460956,0.03352383,0.9396532],"study_design_scores_gemma":[0.0008562438,0.00003911099,0.009755689,0.0002309191,0.0001002448,0.000001190059,0.00005079861,0.9804881,0.0003547298,0.0003230493,0.00757988,0.0002200297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1207363,0.00008770029,0.8740634,0.00003115782,0.0008157907,0.0005971933,0.00004975124,0.00318442,0.0004342432],"genre_scores_gemma":[0.9776441,0.0001537807,0.02105603,0.0001211995,0.0001590588,0.0003657322,0.0004421416,0.00002491949,0.00003304383],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9598811,"threshold_uncertainty_score":0.6052113,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02924965861346095,"score_gpt":0.2422021171611413,"score_spread":0.2129524585476804,"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."}}