{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001083672,0.0001017881,0.00007595435,0.0001216697,0.00005108219,0.0001085008,0.00003983953,0.00005202474,0.00005016649],"category_scores_gemma":[0.000007914218,0.00009819636,0.0000212788,0.0001238922,0.00001461714,0.0002578149,0.00003453558,0.0001993981,0.000007857348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005262317,"about_ca_system_score_gemma":0.000006187206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004357537,"about_ca_topic_score_gemma":0.00004740523,"domain_scores_codex":[0.9995044,0.00001306286,0.0001253668,0.0001428452,0.00009331061,0.0001210239],"domain_scores_gemma":[0.9998631,0.00001304863,0.000006010392,0.00006418509,0.000007726187,0.0000458816],"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":[0.000008691695,0.00003083456,0.004678788,0.0004232845,0.000189544,0.00004251987,0.0008840463,0.3096101,0.004662633,0.002034555,0.009265807,0.6681692],"study_design_scores_gemma":[0.00006018888,0.00001986587,0.005507279,0.00004630502,0.0000219105,0.00001425585,0.00003237381,0.9851671,0.0002713401,0.00001137432,0.008755552,0.00009240212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5900675,0.001000811,0.3807241,0.0000480723,0.0006476313,0.0001864351,0.000006591944,0.02230236,0.005016422],"genre_scores_gemma":[0.9983398,0.0005782594,0.0008062159,0.00001335826,0.00005514623,0.000007986091,0.00001073207,0.00001799515,0.0001705122],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6755571,"threshold_uncertainty_score":0.4004332,"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."}}