{"id":"W4408308472","doi":"10.1155/atr/5065270","title":"Investigating Factors Contributing to Urban Traffic Incident Risk Using High‐Resolution Heterogeneous Data","year":2025,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic and Road Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Shenzhen Science and Technology Innovation Program; Hebei Province Science and Technology Support Program; National Natural Science Foundation of China","keywords":"Transport engineering; Computer science; Engineering","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":[0.001293695,0.0003998756,0.0002893509,0.002442652,0.0003711288,0.0008264045,0.000443221,0.0002943428,0.0006558695],"category_scores_gemma":[0.004596055,0.0002142517,0.0005966946,0.003346517,0.0002877855,0.0009666064,0.001036331,0.0003575564,0.0001230153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005804042,"about_ca_system_score_gemma":0.0005795904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03349344,"about_ca_topic_score_gemma":0.03621252,"domain_scores_codex":[0.9989375,0.0003769333,0.0001031743,0.0002225075,0.0002236237,0.0001362945],"domain_scores_gemma":[0.9971467,0.001117048,0.0007826554,0.0004352301,0.0003549343,0.0001635159],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000261467,0.00005254448,0.9830376,0.00001815801,0.00009537553,0.00008472906,0.0001657687,0.00976901,0.000231716,0.0002509197,0.000164606,0.006103399],"study_design_scores_gemma":[0.000004528477,0.00003508025,0.9326249,0.00001189073,0.00005863145,0.00004544877,0.001408479,0.06443762,0.0003230406,0.0004767682,0.0005529569,0.00002058422],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969009,0.00003161777,0.001908415,0.00004603447,0.000002807719,0.00001372063,0.0008167318,0.00001619832,0.0002635358],"genre_scores_gemma":[0.9974451,0.0000284046,0.0007172767,0.000004202207,0.000003626207,0.00001182718,0.001696527,0.000001911025,0.00009117306],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03349344,"threshold_uncertainty_score":0.06659698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01465431559046011,"score_gpt":0.2530950597125044,"score_spread":0.2384407441220443,"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."}}