{"id":"W397639302","doi":"10.13001/1081-3810.1624","title":"On the Kemeny constant and stationary distribution vector for a Markov chain","year":2014,"lang":"en","type":"article","venue":"Electronic Journal of Linear Algebra","topic":"Matrix Theory and Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Mathematics; Markov chain; Constant (computer programming); Stationary distribution; Distribution (mathematics); Chain (unit); Continuous-time Markov chain; Markov property; Applied mathematics; Combinatorics; Statistical physics; Pure mathematics; Mathematical analysis; Markov model; Statistics; Computer science; Physics","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":[0.001775336,0.00008140125,0.0001203052,0.00003550165,0.0001552493,0.00004878539,0.0002918636,0.00002577413,0.00001289309],"category_scores_gemma":[0.0002470209,0.00005155605,0.00006047437,0.0001054954,0.00004049266,0.0001329844,0.00002988214,0.0002165522,0.000002778566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000552202,"about_ca_system_score_gemma":0.0001555712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":4.926613e-7,"about_ca_topic_score_gemma":6.869317e-7,"domain_scores_codex":[0.9990973,0.0001445308,0.0002231552,0.0001058261,0.0001735326,0.0002556136],"domain_scores_gemma":[0.9987751,0.000746663,0.0001936152,0.000134608,0.0001005241,0.00004953726],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005636076,0.00002559057,0.000004401948,0.0000049834,0.00002503872,0.000001055343,0.00006726495,0.00001692101,0.0001751761,0.9860992,0.0005025578,0.01302146],"study_design_scores_gemma":[0.001272853,0.002229642,0.0001802005,0.00005507159,0.00002291929,0.000167828,0.00004973873,0.11305,0.002515668,0.859569,0.02071414,0.0001729563],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06057825,0.0004004985,0.9334232,0.005220518,0.0001371523,0.0001356614,0.000008997733,0.00001212459,0.00008360687],"genre_scores_gemma":[0.9967794,0.00006184496,0.00246389,0.0003797914,0.0002160661,0.000004397973,0.000003616077,0.000005463193,0.0000854977],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9362012,"threshold_uncertainty_score":0.2102395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005698658319957989,"score_gpt":0.2231411339679574,"score_spread":0.2174424756479994,"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."}}