{"id":"W2591988823","doi":"","title":"TGDB: towards a benchmark for graph databases","year":2016,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Graph Theory and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Computer science; Graph database; Wait-for graph; Graph; Theoretical computer science; Data mining; Database","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009405194,0.0001585499,0.0001463104,0.000270648,0.0002580357,0.0002065855,0.0009789782,0.00002374393,0.000001910063],"category_scores_gemma":[0.0003003413,0.0001104267,0.00005175005,0.0006819555,0.0001744799,0.001439896,0.0004799968,0.0000497799,0.000003049812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002381112,"about_ca_system_score_gemma":0.00009120815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002012273,"about_ca_topic_score_gemma":2.643883e-7,"domain_scores_codex":[0.9985439,0.0000107897,0.0001500235,0.0005600529,0.0002925657,0.000442664],"domain_scores_gemma":[0.9988312,0.0003106274,0.00003277336,0.0004791304,0.0001563001,0.0001900001],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002494918,0.00002315059,0.0004944,0.0000385922,0.00001103879,0.00000963266,0.0003078642,0.0002676735,0.001830239,0.1058448,0.0003088768,0.8908612],"study_design_scores_gemma":[0.002651265,0.0007919981,0.03355157,0.0008459975,0.00002902369,0.0002909874,0.00001937724,0.8518347,0.03072284,0.03762075,0.0390842,0.002557343],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01613142,0.0001424542,0.9821171,0.000193005,0.0009455078,0.0001303235,0.00001012346,0.0003241762,0.000005930298],"genre_scores_gemma":[0.2356519,0.00002213477,0.7639236,0.0001967133,0.0001521703,0.00002766486,9.517843e-7,0.000008626116,0.00001623363],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8883039,"threshold_uncertainty_score":0.4503071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01322364605018729,"score_gpt":0.2217163676464122,"score_spread":0.2084927215962249,"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."}}