{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000273951,0.00007831738,0.0001017464,0.00009095945,0.00005922094,0.0001060243,0.0006079461,0.00003091857,0.00002825979],"category_scores_gemma":[0.00001887586,0.00006831177,0.00002441474,0.0004571569,0.00001517001,0.0002418396,0.0001642957,0.00006303962,0.00009509624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001016754,"about_ca_system_score_gemma":0.00001186187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001032713,"about_ca_topic_score_gemma":0.00008683415,"domain_scores_codex":[0.9991986,0.00003059667,0.0001572711,0.0002933028,0.0001137091,0.0002065153],"domain_scores_gemma":[0.9993096,0.00007712002,0.00002795403,0.0004997569,0.00002313178,0.00006244135],"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":[2.192267e-7,0.00004590871,0.004485426,0.000002439411,0.000002892155,0.000001847136,0.0001780798,0.00003253994,0.00004962076,0.01651393,0.005288664,0.9733984],"study_design_scores_gemma":[0.000233712,0.00002519684,0.01632375,0.00001267364,0.000001104729,0.000004572549,0.00001507705,0.9581433,0.0003268537,0.001641579,0.02313513,0.0001370667],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01321911,0.000009691377,0.9822807,0.001447576,0.00009647289,0.00005547974,0.000005629267,0.0001402941,0.002745087],"genre_scores_gemma":[0.5178928,0.000004629082,0.48069,0.001053541,0.00003551731,0.00003592459,0.00002969273,0.000007138779,0.0002506481],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9732614,"threshold_uncertainty_score":0.2785673,"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."}}