{"id":"W4411624672","doi":"10.1145/3703323.3703749","title":"Road traffic accident severity prediction using causal inference and machine learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Inference; Causal inference; Artificial intelligence; Machine learning; Traffic accident; Accident (philosophy); Road traffic; Transport engineering; Engineering; Statistics; Mathematics","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":[0.005472276,0.0006803364,0.0008147695,0.003262548,0.000527988,0.0008915408,0.001120087,0.0007963948,0.001651153],"category_scores_gemma":[0.01927572,0.0003757095,0.001403969,0.001617879,0.0005395169,0.001212054,0.0007779637,0.00162516,0.000161394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001360712,"about_ca_system_score_gemma":0.00179464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01640973,"about_ca_topic_score_gemma":0.02038225,"domain_scores_codex":[0.9978509,0.001281345,0.0001248271,0.0003793122,0.0002211878,0.0001426093],"domain_scores_gemma":[0.9833552,0.01334301,0.001550803,0.0006889582,0.0008414222,0.0002206566],"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":[0.0002147879,0.0004431868,0.08212072,0.0001566678,0.0003739407,0.0001598639,0.0001505124,0.8079689,0.000349589,0.01179203,0.002124886,0.09414487],"study_design_scores_gemma":[0.00001022077,0.00002229104,0.003245193,0.00001597479,0.00003571176,0.00001186125,0.0000296288,0.9855878,0.0001673902,0.01058505,0.0002808599,0.000008030544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2525123,0.001181992,0.7381852,0.002488792,0.0001600351,0.0002513876,0.001957842,0.000932291,0.002330055],"genre_scores_gemma":[0.9408847,0.0003287651,0.05642604,0.0001512503,0.0001405379,0.000108792,0.001468235,0.00001805752,0.0004736046],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01640973,"threshold_uncertainty_score":0.03262842,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01785227888485614,"score_gpt":0.2448891003736862,"score_spread":0.2270368214888301,"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."}}