{"id":"W4390605291","doi":"10.1145/3632903","title":"Probabilistic Programming Interfaces for Random Graphs: Markov Categories, Graphons, and Nominal Sets","year":2024,"lang":"en","type":"article","venue":"Proceedings of the ACM on Programming Languages","topic":"Logic, Reasoning, and Knowledge","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Air Force Office of Scientific Research; Defense Advanced Research Projects Agency; Ministry of Science and ICT, South Korea; National Research Foundation of Korea; National Research Foundation; Royal Society; Semiconductor Research Corporation","keywords":"Probabilistic logic; Markov chain; Discrete mathematics; Computer science; Graph; Measure (data warehouse); Random graph; Mathematics; Theoretical computer science; Artificial intelligence; Data mining; Statistics","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":[0.002777644,0.0006428227,0.0005490716,0.001882309,0.001694652,0.003767852,0.001939934,0.001989044,0.004745166],"category_scores_gemma":[0.007465621,0.0004813654,0.001544138,0.001781663,0.005852127,0.01259784,0.003524061,0.003259302,0.0006298064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003234542,"about_ca_system_score_gemma":0.001119548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00290107,"about_ca_topic_score_gemma":0.002202015,"domain_scores_codex":[0.9977099,0.0008889539,0.0001232042,0.0004138181,0.0006427428,0.000221386],"domain_scores_gemma":[0.995265,0.002645347,0.0004966913,0.0007019569,0.0005360949,0.0003550464],"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":[0.000002756378,0.000003472452,0.00005136195,0.000006046127,0.000001430109,0.00001525406,0.0000993618,0.0008491565,0.00008432999,0.9978382,0.0001652938,0.0008834114],"study_design_scores_gemma":[0.000002691385,0.000003906881,0.00004007137,0.00000600758,0.000002864472,0.00002511824,0.00005083634,0.009737386,0.0001428934,0.9880585,0.001923357,0.00000641534],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04686134,0.0005116765,0.9252263,0.003568819,0.0001319765,0.00005277816,0.0002186359,0.0004584139,0.02297002],"genre_scores_gemma":[0.7956656,0.0007270966,0.1887244,0.001129037,0.0003139552,0.0003277855,0.0003748593,0.0002824836,0.01245489],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004745166,"threshold_uncertainty_score":0.02346832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01335425872438527,"score_gpt":0.2664779061337499,"score_spread":0.2531236474093647,"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."}}