{"id":"W2145972416","doi":"10.1109/acc.2007.4282703","title":"Synthesis Method for Hierarchical Interface-based Supervisory Control","year":2007,"lang":"en","type":"article","venue":"Proceedings of the ... American Control Conference/Proceedings of the American Control Conference","topic":"Petri Nets in System Modeling","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Supervisor; Controllability; Computer science; Supervisory control; Set (abstract data type); Interface (matter); Algorithm; Construct (python library); High-level synthesis; Theoretical computer science; Control (management); Programming language; Distributed computing; Embedded system; Mathematics; Artificial intelligence; Operating system; Applied 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":["metaepi_narrow","sts","open_science"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.00630503,0.001418,0.004038041,0.0008422883,0.0006997874,0.0006691462,0.01163991,0.0002480136,0.00001910441],"category_scores_gemma":[0.004585108,0.001010313,0.001633331,0.003150501,0.003984517,0.001032673,0.0009570783,0.001319065,0.000006251998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003952467,"about_ca_system_score_gemma":0.001062584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000740298,"about_ca_topic_score_gemma":0.00003756097,"domain_scores_codex":[0.9901943,0.0001672605,0.002911065,0.00213722,0.002306761,0.002283422],"domain_scores_gemma":[0.9775486,0.005044777,0.008206752,0.001329506,0.007176426,0.0006939211],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.007287623,0.0007178412,0.04690719,0.0007694346,0.001639741,0.000002150185,0.0017185,0.000535105,0.7158932,0.120408,0.001111266,0.1030099],"study_design_scores_gemma":[0.01461515,0.003237117,0.03376723,0.00210084,0.001800906,0.0001228931,0.005979008,0.7742689,0.1485055,0.01043821,0.001898618,0.003265561],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2879769,0.0001495615,0.6943851,0.01065533,0.0005120852,0.003042533,0.0002162216,0.0003505248,0.002711766],"genre_scores_gemma":[0.9510348,0.00002598242,0.04489071,0.002947729,0.0002847193,0.0005637868,6.492154e-7,0.0001315003,0.0001201483],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7737339,"threshold_uncertainty_score":0.999857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02601238522704165,"score_gpt":0.2762240510593806,"score_spread":0.2502116658323389,"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."}}