{"id":"W4401072769","doi":"10.1109/memea60663.2024.10596848","title":"Assessing Driving Risk Indicators from Large Driving Data Sets","year":2024,"lang":"en","type":"article","venue":"","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ottawa Hospital; Élisabeth Bruyère Hospital; National Research Council Canada; Carleton University","funders":"","keywords":"Computer science","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":[0.004501253,0.0008655591,0.0007394605,0.003298505,0.0005242939,0.001205576,0.001207479,0.001365884,0.0005475366],"category_scores_gemma":[0.02060462,0.0004172499,0.001446422,0.002789706,0.0004061793,0.001446898,0.0011762,0.001199771,0.000485616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001143976,"about_ca_system_score_gemma":0.0009168844,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01982109,"about_ca_topic_score_gemma":0.02053493,"domain_scores_codex":[0.9969444,0.001118688,0.0003509565,0.0007374673,0.0006412569,0.0002072085],"domain_scores_gemma":[0.9869266,0.007452536,0.001675978,0.00195267,0.001608075,0.0003841968],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003078518,0.0008835149,0.7790858,0.0004188275,0.0008833999,0.0004414498,0.0004202177,0.1614296,0.0009739522,0.001338775,0.007962991,0.04585375],"study_design_scores_gemma":[0.00003423033,0.0003371721,0.6375102,0.00009934742,0.0001104306,0.000387786,0.0007463819,0.351665,0.001004566,0.002713099,0.005313834,0.00007799325],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9559036,0.0003760097,0.01137553,0.0005369235,0.00007188847,0.0001752999,0.03043028,0.0003776628,0.0007528191],"genre_scores_gemma":[0.8982568,0.0002253338,0.01116103,0.00006854332,0.00004241761,0.0002154584,0.08964518,0.00002594213,0.0003592641],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01982109,"threshold_uncertainty_score":0.03941143,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01580844438532811,"score_gpt":0.2788098606853855,"score_spread":0.2630014163000575,"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."}}