{"id":"W4406206083","doi":"10.4230/lipics.icdt.2024.15","title":"Conjunctive Queries on Probabilistic Graphs: The Limits of Approximability","year":2024,"lang":"en","type":"article","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Simons Institute for the Theory of Computing, University of California Berkeley; Ministry of Education, India; Agence Nationale de la Recherche; National Research Foundation; Deutsche Forschungsgemeinschaft; National Research Foundation Singapore","keywords":"Probabilistic logic; Computer science; Conjunctive query; Theoretical computer science; Artificial intelligence; Information retrieval; Relational database","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":[0.017576,0.001924402,0.003626498,0.0019951,0.001998225,0.01128444,0.006988209,0.004197717,0.004812851],"category_scores_gemma":[0.1054617,0.00194793,0.005994954,0.003462932,0.006395416,0.03354369,0.007347955,0.01208067,0.001014425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006948785,"about_ca_system_score_gemma":0.002951447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006077833,"about_ca_topic_score_gemma":0.00405736,"domain_scores_codex":[0.9749619,0.00719354,0.001643841,0.005919985,0.007872631,0.002408152],"domain_scores_gemma":[0.8290335,0.1424006,0.004457919,0.01890858,0.003613051,0.001586259],"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":[0.0009982216,0.0003176798,0.004464353,0.0008882212,0.0004059894,0.0003992408,0.00176759,0.203847,0.003109396,0.7110716,0.006565107,0.06616555],"study_design_scores_gemma":[0.00006914724,0.00004537139,0.0003378367,0.00006005119,0.0001161089,0.0001985311,0.0001613498,0.4146361,0.001401856,0.5807809,0.002163699,0.00002911445],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09549912,0.003576815,0.8737667,0.01115831,0.0001582473,0.0002175505,0.00106798,0.002302338,0.01225295],"genre_scores_gemma":[0.6903042,0.00211681,0.2970347,0.002030179,0.0008913961,0.0004663213,0.001217043,0.001123058,0.004816324],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.017576,"threshold_uncertainty_score":0.09295183,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02464285751339443,"score_gpt":0.2679624310476123,"score_spread":0.2433195735342179,"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."}}