{"id":"W2899147640","doi":"","title":"An Empirical Study of Methods for SPN Learning and Inference","year":2018,"lang":"en","type":"article","venue":"Probabilistic Graphical Models","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; University of Regina","funders":"","keywords":"Computer science; Inference; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009544268,0.0001219456,0.000255192,0.0001328689,0.0001385695,0.00007492325,0.0003598102,0.0000771218,0.00001110527],"category_scores_gemma":[0.0009940241,0.0001002846,0.0000490056,0.0003552653,0.0002038598,0.0002472448,0.0001118957,0.00012676,0.000001092896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007202447,"about_ca_system_score_gemma":0.00003258746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000135941,"about_ca_topic_score_gemma":0.00001347445,"domain_scores_codex":[0.9984918,0.0003901305,0.0002983579,0.0004717808,0.0001441814,0.0002037075],"domain_scores_gemma":[0.9980185,0.001131487,0.0000785388,0.0003007205,0.0003195232,0.0001512153],"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":[0.0000847971,0.001862602,0.01078649,0.00008008005,0.0000576377,0.000001600254,0.003891413,0.0001583332,0.001006291,0.18197,0.0000139973,0.8000867],"study_design_scores_gemma":[0.000265058,0.001233058,0.001580241,0.000008608899,0.0000157822,0.000002679838,0.0000713003,0.6221981,0.0001607835,0.3743169,0.00004535083,0.0001021695],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2475964,0.00001268439,0.7515795,0.00008997467,0.0000472822,0.0003515399,8.279422e-7,0.00007320826,0.0002485484],"genre_scores_gemma":[0.6433181,0.000002664538,0.3565535,0.00004476934,0.00001780163,0.00005430791,3.882014e-7,0.000004892453,0.000003542463],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7999846,"threshold_uncertainty_score":0.4089486,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1101625039061681,"score_gpt":0.4259945006744092,"score_spread":0.3158319967682411,"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."}}