{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07888181,0.00238004,0.002398009,0.004009867,0.001796341,0.003603094,0.006262287,0.004395455,0.008453873],"category_scores_gemma":[0.3629445,0.001570235,0.002789994,0.003796657,0.005811171,0.01400488,0.005187638,0.007791521,0.0008106391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003292119,"about_ca_system_score_gemma":0.002596385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008232944,"about_ca_topic_score_gemma":0.008297862,"domain_scores_codex":[0.9650451,0.02793948,0.001002439,0.002747256,0.002757485,0.0005082591],"domain_scores_gemma":[0.3567132,0.6140142,0.004688465,0.01736353,0.005879018,0.001341549],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00115141,0.000491281,0.03068307,0.001372268,0.001351196,0.000291244,0.001076333,0.3150942,0.0009753709,0.3813008,0.01037856,0.2558343],"study_design_scores_gemma":[0.0001011846,0.0001610482,0.00296986,0.0002873354,0.0001473612,0.0003561477,0.0002026401,0.7640078,0.0006032425,0.2280183,0.003092149,0.00005289724],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04670438,0.005988003,0.9394851,0.002474841,0.0001683268,0.0001542351,0.0004349507,0.0005317101,0.004058418],"genre_scores_gemma":[0.5648026,0.005401975,0.4170009,0.001093047,0.0009021986,0.0006470347,0.002709146,0.001129532,0.006313423],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.07888181,"threshold_uncertainty_score":0.4171718,"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."}}