{"id":"W2575561059","doi":"10.1609/aaai.v30i1.9957","title":"Decision Sum-Product-Max Networks","year":2016,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Graphical model; Computer science; Influence diagram; Inference; Product (mathematics); Decision tree; Probabilistic logic; Treewidth; Class (philosophy); Extension (predicate logic); Mathematical optimization; Theoretical computer science; Artificial intelligence; Mathematics; Graph","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":[0.003257464,0.001356322,0.001994387,0.0008886288,0.0009662653,0.001950374,0.002907671,0.001948828,0.009385959],"category_scores_gemma":[0.01304431,0.001144842,0.001377211,0.00151915,0.001856716,0.005833838,0.002408515,0.00281576,0.001180045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001383468,"about_ca_system_score_gemma":0.00134576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001735483,"about_ca_topic_score_gemma":0.002713118,"domain_scores_codex":[0.9978237,0.0008912073,0.0001437762,0.0006930361,0.0002921151,0.000156115],"domain_scores_gemma":[0.9934797,0.004860436,0.0003746904,0.0005840524,0.0004930819,0.0002080174],"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":[0.0004187242,0.0001419035,0.001185941,0.0003885122,0.0001678781,0.0001761952,0.0002169842,0.5279357,0.001693474,0.2874628,0.008470942,0.1717409],"study_design_scores_gemma":[0.00002054453,0.00002474308,0.00006033785,0.00001316989,0.00001977867,0.00004026983,0.0000129008,0.7944525,0.0006235993,0.2028925,0.001828216,0.00001143357],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01157039,0.0003464495,0.9835271,0.0003997856,0.00005961821,0.0000807598,0.0003036291,0.0003425204,0.003369735],"genre_scores_gemma":[0.4478256,0.0007604546,0.5392352,0.0006484502,0.0001745961,0.0005047439,0.001336372,0.0002659472,0.009248565],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009385959,"threshold_uncertainty_score":0.03139919,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06666047892119044,"score_gpt":0.2876446630990715,"score_spread":0.2209841841778811,"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."}}