{"id":"W2169354347","doi":"10.1214/105051606000000024","title":"Learning nonsingular phylogenies and hidden Markov models","year":2006,"lang":"en","type":"article","venue":"The Annals of Applied Probability","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Division of Emerging Frontiers; Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies; National Science Foundation","keywords":"Hidden Markov model; Invertible matrix; Markov chain; Bounded function; Markov model; Maximum-entropy Markov model; Variable-order Markov model; Markov process","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004800725,0.0007985591,0.001417227,0.0009476619,0.0008850178,0.001729877,0.001595887,0.002203298,0.001783455],"category_scores_gemma":[0.02944051,0.0007966775,0.0007417783,0.0009309297,0.003511939,0.00616848,0.002418982,0.003672008,0.0003431739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00138862,"about_ca_system_score_gemma":0.00131952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002194247,"about_ca_topic_score_gemma":0.001707645,"domain_scores_codex":[0.9972651,0.001225076,0.0001099788,0.0008183809,0.0003797316,0.000201815],"domain_scores_gemma":[0.9640808,0.03127316,0.001931625,0.001497833,0.0006860989,0.0005305178],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002145745,0.0001194522,0.003097064,0.000227088,0.00008732567,0.0003198118,0.0003076471,0.5769339,0.001416416,0.3776911,0.001365449,0.03822014],"study_design_scores_gemma":[0.00001053381,0.00001737561,0.0001437771,0.000007346633,0.000005442788,0.00002616136,0.00001627084,0.6426328,0.0003284698,0.3564988,0.0003046598,0.00000834099],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06827687,0.0006281265,0.9281471,0.001324082,0.00003895862,0.00002620282,0.0001414842,0.0002735588,0.001143505],"genre_scores_gemma":[0.8375293,0.00104701,0.156874,0.0003715278,0.0002986999,0.00009374578,0.0007142199,0.0001275337,0.002944013],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004800725,"threshold_uncertainty_score":0.02538896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1179329107715778,"score_gpt":0.3360329304513031,"score_spread":0.2181000196797253,"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."}}