{"id":"W4244572977","doi":"10.1109/sc.2018.00024","title":"PruneJuice: Pruning Trillion-edge Graphs to a Precise Pattern-Matching Solution","year":2018,"lang":"en","type":"article","venue":"","topic":"Graph Theory and Algorithms","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Scalability; Computer science; Pruning; Pipeline (software); Matching (statistics); Enhanced Data Rates for GSM Evolution; Scaling; Pattern matching; Set (abstract data type); Analytics; Focus (optics); Graph; Theoretical computer science; Data mining; Algorithm; Artificial intelligence; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005857471,0.0001628714,0.000149217,0.0002646035,0.0003780224,0.0002107051,0.0008228213,0.00005838068,0.00006026988],"category_scores_gemma":[0.00003229796,0.0001395905,0.00008635002,0.0008668651,0.00005494983,0.0007174351,0.0003386548,0.0001176455,0.0003747311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002207598,"about_ca_system_score_gemma":0.00002854583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004998342,"about_ca_topic_score_gemma":0.00002899326,"domain_scores_codex":[0.9985296,0.0001002734,0.0002332596,0.0004717725,0.0002543495,0.0004107643],"domain_scores_gemma":[0.9990086,0.00007944867,0.00006220165,0.0005553096,0.0001127102,0.0001817156],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002961526,0.0002151361,0.0007421287,0.00002918826,0.00005007595,0.00002187315,0.016746,0.00009113845,0.01596055,0.2665311,0.003189757,0.6963934],"study_design_scores_gemma":[0.002073319,0.001819097,0.01348284,0.0004658963,0.00003974084,0.0001390793,0.0007868787,0.1913629,0.0690827,0.7045529,0.01397825,0.002216438],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1268743,0.0000188546,0.8669975,0.0007407832,0.000758648,0.0002198162,0.000001220251,0.0003446151,0.00404426],"genre_scores_gemma":[0.9499824,0.000002517871,0.04844529,0.0008375797,0.0001520382,0.00002234793,0.000001160219,0.00001059868,0.0005460842],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8231081,"threshold_uncertainty_score":0.5692336,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01391192245031823,"score_gpt":0.2536103006430915,"score_spread":0.2396983781927733,"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."}}