{"id":"W2235204424","doi":"10.48550/arxiv.1111.3182","title":"Context Tree Switching","year":2011,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Weighting; Tree (set theory); Context (archaeology); Markov chain; Computer science; Binary tree; Generalization; Mathematics; Class (philosophy); Markov process; Algorithm; Artificial intelligence; Machine learning; Statistics; Combinatorics; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001332208,0.0007134176,0.000753959,0.000928906,0.0007805951,0.001301419,0.001690826,0.0009596879,0.009747889],"category_scores_gemma":[0.007011905,0.0004057633,0.0008065727,0.001390039,0.0005449652,0.002929267,0.002111468,0.001818581,0.002710443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005816857,"about_ca_system_score_gemma":0.001100942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002482269,"about_ca_topic_score_gemma":0.003808946,"domain_scores_codex":[0.9985387,0.0003867417,0.0000665806,0.0003677593,0.0005160028,0.0001242016],"domain_scores_gemma":[0.9977366,0.001094248,0.00009120622,0.0005996209,0.0003751823,0.0001031726],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002684356,0.0001805225,0.002225656,0.0002063026,0.00008930812,0.0003046756,0.0003506731,0.05783329,0.02488819,0.1388399,0.01063305,0.76418],"study_design_scores_gemma":[0.00004066075,0.0001175654,0.0008983152,0.00006070058,0.00007734494,0.0004930535,0.00008008825,0.774237,0.0223903,0.1554855,0.04605682,0.00006260712],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01199375,0.0004400466,0.9786801,0.0001517111,0.0001737478,0.00009654476,0.0002253179,0.001630725,0.006608043],"genre_scores_gemma":[0.410797,0.0007762721,0.5731955,0.0005503641,0.0003176646,0.0003334969,0.001198241,0.0008295083,0.01200199],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009747889,"threshold_uncertainty_score":0.03260994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1424956576611086,"score_gpt":0.1649113809662521,"score_spread":0.02241572330514352,"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."}}