{"id":"W2125388232","doi":"10.1109/icsmc.1993.384935","title":"On modelling non-stationary random environments using switching techniques","year":2002,"lang":"en","type":"article","venue":"","topic":"semigroups and automata theory","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Automaton; Markov chain; Computer science; Learning automata; Continuous-time Markov chain; Stochastic matrix; Stationary distribution; State (computer science); Chain (unit); Theoretical computer science; Stochastic process; Markov process; Artificial intelligence; Markov model; Mathematics; Algorithm; Markov property; Machine learning","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":[0.001697271,0.001074071,0.001119196,0.00104492,0.0006134456,0.001530963,0.001549174,0.001610362,0.003269613],"category_scores_gemma":[0.004228617,0.0005447234,0.001761244,0.001161608,0.003086,0.003747264,0.00165407,0.001803644,0.000754066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009287635,"about_ca_system_score_gemma":0.0007286345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001479161,"about_ca_topic_score_gemma":0.001367378,"domain_scores_codex":[0.9985849,0.0006962564,0.0000795364,0.0002527647,0.0002658122,0.0001207895],"domain_scores_gemma":[0.9961048,0.003030111,0.0002864911,0.000313059,0.0001532209,0.0001122589],"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":[0.00003571901,0.00005037198,0.0004584136,0.0001304931,0.00005171333,0.0001620519,0.0003768911,0.2772853,0.00198742,0.70016,0.0006815283,0.01862002],"study_design_scores_gemma":[0.0000141014,0.00005368579,0.0001110221,0.00002805766,0.00001619952,0.00007013301,0.00003172177,0.5197054,0.0005695611,0.4753095,0.004068894,0.00002174083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006360783,0.0005241056,0.9888376,0.0002165824,0.00004368378,0.00003059667,0.00003406168,0.0001377377,0.003814806],"genre_scores_gemma":[0.4936165,0.004112097,0.4860125,0.0004590214,0.0004572938,0.000539007,0.0003459569,0.0002717271,0.01418595],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003269613,"threshold_uncertainty_score":0.01093793,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02515297446667359,"score_gpt":0.2244084694841082,"score_spread":0.1992554950174346,"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."}}