{"id":"W2883844381","doi":"10.1007/978-3-319-96145-3_39","title":"Deciding Probabilistic Bisimilarity Distance One for Labelled Markov Chains","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Formal Methods in Verification","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Markov chain; Probabilistic logic; Generalization; Equivalence relation; Equivalence (formal languages); Algorithm; Markov process; Mathematics; Discrete mathematics; Total variation; Combinatorics; Computer science; Statistics","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":[0.002890056,0.001782006,0.00193315,0.00180065,0.001691517,0.003312402,0.002615712,0.00257028,0.01040638],"category_scores_gemma":[0.02360809,0.0009646795,0.002965139,0.00135134,0.002206622,0.007699515,0.00453204,0.003800918,0.002591329],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003479666,"about_ca_system_score_gemma":0.004071837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001821788,"about_ca_topic_score_gemma":0.002588478,"domain_scores_codex":[0.9933996,0.001060168,0.0006815093,0.002492401,0.001736767,0.0006295727],"domain_scores_gemma":[0.9807726,0.01368834,0.0009130125,0.002513965,0.001607385,0.0005047342],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.002020034,0.000589964,0.005186362,0.001306044,0.0002813583,0.000521818,0.000842691,0.1680776,0.04692952,0.2655193,0.0139637,0.4947616],"study_design_scores_gemma":[0.0001998162,0.0001978464,0.0006081193,0.00008771749,0.00008837883,0.0003674695,0.0002027448,0.5935014,0.03735417,0.3606914,0.006597901,0.0001030716],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04146368,0.000252178,0.9477977,0.000438061,0.0001061791,0.0003079213,0.000515922,0.004764958,0.004353427],"genre_scores_gemma":[0.250107,0.0001671421,0.7430402,0.0002438394,0.00006654346,0.0003344125,0.001845918,0.0009776076,0.003217264],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01040638,"threshold_uncertainty_score":0.03481281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04381443064443207,"score_gpt":0.2925889104563065,"score_spread":0.2487744798118744,"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."}}