{"id":"W2607217863","doi":"10.1007/978-3-319-57351-9_35","title":"Resolving Inconsistencies of Scope Interpretations in Sum-Product Networks","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"","keywords":"Scope (computer science); Computer science; Interpretation (philosophy); Completeness (order theory); Inference; Consistency (knowledge bases); Product (mathematics); Artificial intelligence; Theoretical computer science; Machine learning; Mathematics; Programming language","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":[0.01144717,0.001275931,0.001866577,0.003651933,0.00326796,0.006630853,0.00498712,0.004298284,0.006883201],"category_scores_gemma":[0.1014326,0.002300626,0.001882223,0.005178492,0.005851517,0.02425464,0.009604248,0.006139253,0.000951299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001970748,"about_ca_system_score_gemma":0.001492906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003027864,"about_ca_topic_score_gemma":0.00352878,"domain_scores_codex":[0.9865152,0.006539432,0.001081997,0.002245703,0.003083311,0.0005343093],"domain_scores_gemma":[0.9207379,0.0643314,0.002944825,0.006842453,0.004305176,0.0008382705],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002816149,0.0000427669,0.002966852,0.0003339909,0.000123946,0.0008798193,0.003608007,0.02123931,0.001150904,0.8965582,0.004122975,0.06869162],"study_design_scores_gemma":[0.00001518698,0.000006434277,0.00013033,0.00007131466,0.00006051184,0.000133685,0.0004120096,0.03579191,0.000894125,0.9592704,0.003196482,0.00001762231],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07892372,0.001199599,0.896823,0.002907077,0.0002031822,0.0001098808,0.0007222711,0.001063225,0.01804804],"genre_scores_gemma":[0.6724677,0.0007071664,0.3198681,0.0004562773,0.0002135008,0.0001689354,0.001270269,0.0008489522,0.003998963],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01144717,"threshold_uncertainty_score":0.06053913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02911845747978071,"score_gpt":0.2686834118744588,"score_spread":0.2395649543946781,"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."}}