{"id":"W2164379666","doi":"10.1109/icdm.2014.51","title":"Heavyweight Pattern Mining in Attributed Flow Graphs","year":2014,"lang":"en","type":"article","venue":"","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; IBM (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Compiler; Enhanced Data Rates for GSM Evolution; Path (computing); Set (abstract data type); Flow (mathematics); Data mining; Theoretical computer science; Algorithm; Mathematics; Artificial intelligence; Programming language","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002966678,0.001087425,0.001389426,0.008252379,0.001471742,0.002361136,0.002678655,0.001558448,0.001051362],"category_scores_gemma":[0.01876666,0.0007285901,0.001833217,0.007352488,0.001368788,0.00589108,0.002354134,0.001997885,0.000423618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001088043,"about_ca_system_score_gemma":0.001895625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004201132,"about_ca_topic_score_gemma":0.00542986,"domain_scores_codex":[0.9944891,0.0009574757,0.0006453307,0.001567947,0.001930716,0.0004094456],"domain_scores_gemma":[0.9829575,0.009478511,0.002547465,0.002654642,0.001855775,0.0005061066],"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":[0.0006686209,0.0008223644,0.07357821,0.001462503,0.0006104786,0.002501991,0.001200996,0.2193725,0.01399539,0.06423949,0.01366597,0.6078816],"study_design_scores_gemma":[0.00005079364,0.0001056311,0.00475597,0.0001321131,0.0001229408,0.001008744,0.0004903059,0.7310492,0.0119955,0.2381498,0.01208395,0.00005503375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06226519,0.0005041619,0.9293174,0.0006161024,0.00006085771,0.0002913116,0.003326904,0.002616418,0.001001638],"genre_scores_gemma":[0.3698764,0.0006616471,0.6190282,0.0003723478,0.0001091003,0.0004122525,0.007227539,0.0003513517,0.001961146],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008252379,"threshold_uncertainty_score":0.01568943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01352545664610084,"score_gpt":0.2310836326789239,"score_spread":0.2175581760328231,"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."}}