{"id":"W2903551170","doi":"10.5555/3291656.3291684","title":"PruneJuice: pruning trillion-edge graphs to a precise pattern-matching solution","year":2018,"lang":"en","type":"article","venue":"IEEE International Conference on High Performance Computing, Data, and Analytics","topic":"Graph Theory and Algorithms","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Scalability; Computer science; Pruning; Pipeline (software); Matching (statistics); Scaling; Enhanced Data Rates for GSM Evolution; Pattern matching; Analytics; Set (abstract data type); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001669146,0.001655905,0.001498981,0.003313517,0.001536902,0.001801116,0.003057734,0.001757128,0.007557523],"category_scores_gemma":[0.01214654,0.000829996,0.001636186,0.003124475,0.001231298,0.003426094,0.002675375,0.002079913,0.002572486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008504409,"about_ca_system_score_gemma":0.002664496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00470915,"about_ca_topic_score_gemma":0.01325275,"domain_scores_codex":[0.9983312,0.0003357806,0.0001175895,0.0005071878,0.0005053352,0.0002028706],"domain_scores_gemma":[0.9956542,0.001825305,0.0003488569,0.001419445,0.0005370707,0.0002150922],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006380033,0.0007234704,0.009247147,0.0009529949,0.0002649015,0.0006658603,0.0008002178,0.1525709,0.02393739,0.04168345,0.04530552,0.7232101],"study_design_scores_gemma":[0.0001264801,0.0002021134,0.0008139402,0.00009875345,0.00008475516,0.000421205,0.0004239361,0.8896874,0.01297799,0.07728876,0.0178456,0.00002903659],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0439357,0.0005700364,0.9424886,0.0007917546,0.0002036181,0.0004242458,0.0008235195,0.007216991,0.003545572],"genre_scores_gemma":[0.1054688,0.0001958796,0.8862645,0.0003858957,0.00004630812,0.0002585233,0.002757214,0.001035067,0.003587721],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007557523,"threshold_uncertainty_score":0.02528238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05831802717286136,"score_gpt":0.318046247010309,"score_spread":0.2597282198374476,"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."}}