{"id":"W3166986506","doi":"10.1145/3469379.3469381","title":"A Deeper Dive into Pattern-Aware Subgraph Exploration with PEREGRINE","year":2021,"lang":"en","type":"article","venue":"ACM SIGOPS Operating Systems Review","topic":"Graph Theory and Algorithms","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Scalability; Graph; Reuse; Theoretical computer science; Computation; Semantics (computer science); Key (lock); Pattern matching; Data mining; Artificial intelligence; Algorithm; Programming language; 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":[0.001672453,0.0008720808,0.0008480355,0.0013015,0.0006319688,0.002275388,0.003174915,0.001038838,0.00540759],"category_scores_gemma":[0.006409786,0.0009332667,0.001462045,0.001973768,0.001413687,0.01039327,0.002145867,0.002546364,0.001460823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009266267,"about_ca_system_score_gemma":0.001472346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002460348,"about_ca_topic_score_gemma":0.007276456,"domain_scores_codex":[0.9983348,0.0005173918,0.0000976513,0.0003624897,0.0005181278,0.0001694184],"domain_scores_gemma":[0.9957956,0.001728503,0.0001882902,0.001943178,0.0002462068,0.00009828902],"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":[0.0006478509,0.0005466643,0.006459338,0.001104362,0.0002580627,0.0003086204,0.001141767,0.05529452,0.04068146,0.1548487,0.03249073,0.7062179],"study_design_scores_gemma":[0.000235783,0.0005779941,0.002466135,0.000348361,0.0001858035,0.001180538,0.0006183952,0.5154231,0.05094152,0.2983034,0.129578,0.0001409723],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05388345,0.002929545,0.9108504,0.003740397,0.0001197196,0.0001375062,0.00051045,0.0179588,0.009869734],"genre_scores_gemma":[0.1511323,0.001347856,0.8400521,0.000826475,0.00005637503,0.00009545124,0.0009642257,0.001708574,0.003816721],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00540759,"threshold_uncertainty_score":0.01809025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02257123520308827,"score_gpt":0.2612624367902642,"score_spread":0.238691201587176,"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."}}