{"id":"W4285813009","doi":"10.1109/iv51971.2022.9827272","title":"Vehicle-to-Everything (V2X) in Scenarios: Extending Scenario Description Language for Connected Vehicle Scenario Descriptions","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Intelligent Vehicles Symposium (IV)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Transport Canada","keywords":"Computer science; Human–computer interaction","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.005987406,0.001214774,0.0006606927,0.002197534,0.0006727854,0.00433466,0.002517629,0.001689256,0.004177316],"category_scores_gemma":[0.006557676,0.0009141976,0.002203256,0.001303041,0.002402922,0.007587345,0.00420066,0.003875796,0.002255194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001761194,"about_ca_system_score_gemma":0.002776523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006276314,"about_ca_topic_score_gemma":0.006313201,"domain_scores_codex":[0.9959679,0.001589646,0.0008624649,0.0004040803,0.0008637279,0.0003122393],"domain_scores_gemma":[0.9956067,0.002211405,0.0003690607,0.0008107697,0.0007352931,0.0002666705],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001654995,0.00009875643,0.001425299,0.001094656,0.00007788584,0.001515699,0.00404967,0.03205364,0.007513768,0.8498307,0.01928632,0.08288806],"study_design_scores_gemma":[0.00008366557,0.0001093109,0.0004999271,0.0009308595,0.00006709812,0.001453201,0.001009829,0.1457065,0.00899036,0.2359226,0.6050594,0.0001671949],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003798396,0.0004238906,0.9801067,0.0007457213,0.0001464201,0.0003359922,0.001647396,0.006189144,0.006606369],"genre_scores_gemma":[0.1030987,0.001688384,0.8760085,0.001272219,0.0001261384,0.001215074,0.007722199,0.002364265,0.006504607],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006276314,"threshold_uncertainty_score":0.03166479,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0175188401406825,"score_gpt":0.2357673056986028,"score_spread":0.2182484655579203,"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."}}