{"id":"W4288420235","doi":"10.48550/arxiv.1903.02076","title":"Optimizing Subgraph Queries by Combining Binary and Worst-Case Optimal\\n Joins","year":2019,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Joins; Computer science; Intersection (aeronautics); Binary number; Query optimization; Query plan; Matching (statistics); Vertex (graph theory); Partition (number theory); Theoretical computer science; Graph; Mathematics; Sargable; Data mining; Search engine; Combinatorics; Information retrieval","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.00081995,0.001069537,0.00100511,0.0008825011,0.001000631,0.00147226,0.00284358,0.0004723153,0.0001408812],"category_scores_gemma":[0.00003232063,0.001349653,0.0003779003,0.001625909,0.0007240182,0.003584316,0.01100461,0.00124532,0.0001760888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001874779,"about_ca_system_score_gemma":0.0001794156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005018095,"about_ca_topic_score_gemma":0.00002728609,"domain_scores_codex":[0.994321,0.0003626238,0.0005999684,0.003315262,0.0002373432,0.00116381],"domain_scores_gemma":[0.995686,0.000278173,0.0007356988,0.002567269,0.0001986978,0.0005341645],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004333973,0.001400193,0.01240236,0.001667389,0.002110875,0.04669829,0.006544059,0.601349,0.0001210003,0.3006041,0.01650238,0.010167],"study_design_scores_gemma":[0.001738177,0.0003632033,0.0002217091,0.0004509979,0.0003531034,0.0001834541,0.002221722,0.9846647,0.00004769481,0.001485459,0.00656355,0.001706236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4378229,0.0007106336,0.5547235,0.0002405775,0.001747891,0.0009024355,0.0001598571,0.0003249798,0.003367167],"genre_scores_gemma":[0.976026,0.004160726,0.01151821,0.0001964878,0.00007916747,0.000002272648,0.0001481748,0.00006639717,0.007802544],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5432053,"threshold_uncertainty_score":0.9995643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05482722818346512,"score_gpt":0.1823133907262503,"score_spread":0.1274861625427852,"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."}}