{"id":"W2767318079","doi":"10.1109/models.2017.38","title":"Synthesis and Exploration of Multi-level, Multi-perspective Architectures of Automotive Embedded Systems (SoSYM Abstract)","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Software Engineering Methodologies","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Automotive industry; Computer science; Process (computing); Perspective (graphical); Domain (mathematical analysis); Set (abstract data type); Task (project management); Focus (optics); Embedded system; Computer architecture; Distributed computing; Software engineering; Systems engineering; Artificial intelligence; Engineering; Programming language","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":[0.0005366107,0.0001572109,0.0003379366,0.0001288273,0.0001061977,0.00006442225,0.0006437735,0.00007349136,0.000001331502],"category_scores_gemma":[0.006628546,0.0001302633,0.00004781752,0.0000620814,0.0001872364,0.0005901483,0.0002364312,0.0001055079,0.000001151967],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003662231,"about_ca_system_score_gemma":0.00003326845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001987646,"about_ca_topic_score_gemma":0.00002600972,"domain_scores_codex":[0.9989665,0.0001063551,0.0002675071,0.0003264788,0.0001710297,0.0001621829],"domain_scores_gemma":[0.9972128,0.001351916,0.0003582273,0.0007292772,0.0003002446,0.00004758636],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007889885,0.0004985842,0.001133477,0.0007279314,0.0004558776,0.00003576365,0.04279538,0.7030488,0.06684642,0.04949784,0.0000325539,0.1348484],"study_design_scores_gemma":[0.0009481763,0.0001666225,0.1560648,0.0003609706,0.00003191759,0.00001611981,0.004763045,0.1780491,0.6515957,0.007411267,0.000005724301,0.0005866032],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01982054,0.0001945428,0.9791641,0.00007229485,0.0002263223,0.0002190763,0.00001185832,0.0001776735,0.0001136324],"genre_scores_gemma":[0.5329597,0.00001049262,0.4669606,0.000001827862,0.000008412642,0.00001458598,1.095749e-7,0.000005953664,0.00003834536],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5847492,"threshold_uncertainty_score":0.7935467,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1616229201916998,"score_gpt":0.3608000031948969,"score_spread":0.1991770830031971,"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."}}