{"id":"W4388623088","doi":"10.1109/tse.2023.3331254","title":"Concretization of Abstract Traffic Scene Specifications Using Metaheuristic Search","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Software Engineering","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Nemzeti Kutatási, Fejlesztési és Innovaciós Alap; Natural Sciences and Engineering Research Council of Canada; National Research, Development and Innovation Office; Innovációs és Technológiai Minisztérium; Nemzeti Kutatási Fejlesztési és Innovációs Hivatal","keywords":"Computer science; Context (archaeology); Scalability; Constraint (computer-aided design); Metaheuristic; Set (abstract data type); Real-time computing; Artificial intelligence; Programming language; Database","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001182588,0.001454004,0.0006717239,0.001167803,0.0004188827,0.001031398,0.001428353,0.0009286021,0.002357051],"category_scores_gemma":[0.004124529,0.0006723938,0.00146429,0.0006267605,0.001007382,0.0009917034,0.001680787,0.001109689,0.0002853364],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001084397,"about_ca_system_score_gemma":0.001831474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005948362,"about_ca_topic_score_gemma":0.009133617,"domain_scores_codex":[0.9990767,0.000353333,0.00005069654,0.000133437,0.0002735741,0.0001122094],"domain_scores_gemma":[0.99803,0.001154808,0.0002140243,0.0002681125,0.0002731788,0.00005984437],"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":[0.00005879327,0.00009708147,0.0009299529,0.0001044509,0.00003947529,0.0001337231,0.0001051286,0.9527079,0.006028825,0.0070866,0.000780269,0.03192785],"study_design_scores_gemma":[0.00001810421,0.00003728403,0.0001053944,0.00001033023,0.00000890403,0.00002056853,0.00006412276,0.9938438,0.002399717,0.002639925,0.0008455734,0.000006280775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09028085,0.00008279387,0.9026085,0.0001531738,0.00002372387,0.0003001206,0.0002972993,0.002333729,0.003919827],"genre_scores_gemma":[0.4123677,0.00009804481,0.5834281,0.0001494134,0.00001046793,0.0003302638,0.001556992,0.0006302554,0.001428796],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005948362,"threshold_uncertainty_score":0.01182747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04044585752119628,"score_gpt":0.2386147782035572,"score_spread":0.1981689206823609,"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."}}