{"id":"W2159227863","doi":"10.1109/icdcs.2013.66","title":"Towards an Efficient Online Causal-Event-Pattern-Matching Framework","year":2013,"lang":"en","type":"article","venue":"","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Event (particle physics); Matching (statistics); Identification (biology); Pattern matching; Data mining; Class (philosophy); Process (computing); Artificial intelligence; Mathematics","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":[0.0003293431,0.0001788448,0.0002038184,0.00006807199,0.000150035,0.0002451674,0.0009249221,0.0001247596,0.0002630252],"category_scores_gemma":[0.00003944339,0.0001247551,0.00008068316,0.000288026,0.00003351219,0.0005852262,0.0003007415,0.0002355953,0.0006673384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006084176,"about_ca_system_score_gemma":0.00007332108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001336067,"about_ca_topic_score_gemma":0.00002510177,"domain_scores_codex":[0.9983308,0.00007652811,0.0003406792,0.0004744881,0.0003917431,0.0003857213],"domain_scores_gemma":[0.9984795,0.000074738,0.00007562676,0.001043149,0.0001319944,0.0001949417],"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":[0.000006545917,0.002124996,0.04327401,0.0002928025,0.00008178278,0.00002893689,0.01195064,0.02809957,0.0005906926,0.02711715,0.003202022,0.8832309],"study_design_scores_gemma":[0.0003965832,0.0003099001,0.270384,0.0001941683,0.00000848978,0.00004326291,0.0005199242,0.7105021,0.001052038,0.01493813,0.0008813283,0.0007699928],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4578466,0.00002343916,0.5400271,0.0006568485,0.0006370203,0.0001757445,0.000001201652,0.0003117123,0.0003202551],"genre_scores_gemma":[0.9313337,0.000003457784,0.06724373,0.001051296,0.0001900642,0.00002677814,0.000004369181,0.0000101836,0.0001364156],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8824608,"threshold_uncertainty_score":0.8577508,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01299106516033919,"score_gpt":0.2752542526844332,"score_spread":0.262263187524094,"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."}}