{"id":"W4287284579","doi":"10.48550/arxiv.2103.02284","title":"Columnar Storage and List-based Processing for Graph Database Management\\n Systems","year":2021,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Graph Theory and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Joins; Scalability; Column (typography); Block (permutation group theory); Graph; Query optimization; Database; Online aggregation; Parallel computing; Relational database management system; Set (abstract data type); Theoretical computer science; Sargable; Relational database; Information retrieval; Web search query; Programming language; Search engine; Computer network","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"],"consensus_categories":[],"category_scores_codex":[0.001241227,0.0008032088,0.0008381424,0.0007408458,0.0012359,0.001556005,0.002245979,0.000378051,0.00002568637],"category_scores_gemma":[0.00003185156,0.001052985,0.000435031,0.001946616,0.0005494398,0.001361932,0.002314002,0.0006640819,0.000007789642],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001724172,"about_ca_system_score_gemma":0.0003293802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001112379,"about_ca_topic_score_gemma":0.00003698009,"domain_scores_codex":[0.9946288,0.0005266861,0.0005253099,0.00318039,0.0002250282,0.0009137553],"domain_scores_gemma":[0.9959925,0.0002684105,0.0006823232,0.002041656,0.0005084468,0.0005066421],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005224394,0.001265497,0.001557058,0.01521662,0.001003712,0.005021988,0.001210659,0.4577255,0.0001901155,0.5081165,0.0002364007,0.007933501],"study_design_scores_gemma":[0.002257856,0.0001530275,0.0002089921,0.001939186,0.0005860741,0.0000201891,0.001899087,0.9838824,0.00008950135,0.006880132,0.0008664754,0.001217088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1149724,0.001069517,0.8797437,0.00004114463,0.001490634,0.001652501,0.0001938205,0.0001997039,0.0006365427],"genre_scores_gemma":[0.9868051,0.00048009,0.009958815,0.0001115547,0.0001093309,0.00001730409,0.0001660038,0.000053965,0.002297833],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8718327,"threshold_uncertainty_score":0.9994805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05828580466625022,"score_gpt":0.1879110786274366,"score_spread":0.1296252739611863,"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."}}