{"id":"W2506101519","doi":"10.1142/9789814417747_0145","title":"UAPRIORI: AN ALGORITHM FOR FINDING SEQUENTIAL PATTERNS IN PROBABILISTIC DATA","year":2012,"lang":"en","type":"book-chapter","venue":"World Scientific proceedings series on computer engineering and information science","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Data mining; Computer science; Probabilistic logic; Cluster analysis; Uncertain data; Ranking (information retrieval); Machine learning; Artificial intelligence","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.002048639,0.0003581223,0.0003022111,0.001521489,0.0005635463,0.003779415,0.002783947,0.00009158473,0.000005269721],"category_scores_gemma":[0.00004400556,0.0003602622,0.00003565667,0.0007472089,0.0002832607,0.02018514,0.001283308,0.0003132914,0.00002016276],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001607336,"about_ca_system_score_gemma":0.0001872007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003768668,"about_ca_topic_score_gemma":0.000005445195,"domain_scores_codex":[0.9971043,0.000002240513,0.0006042265,0.0009541062,0.0007477655,0.0005873956],"domain_scores_gemma":[0.9981048,0.00005187115,0.0002945545,0.0009040907,0.0003685012,0.0002761817],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002657511,0.0000255492,0.00000776365,0.0001705407,0.000007368328,5.447508e-7,0.001254545,0.0004535081,0.00003906622,0.5137938,0.000853273,0.4833914],"study_design_scores_gemma":[0.0001405832,0.00006355144,0.00008649946,0.0002288338,0.000006469256,0.00001867236,0.00001385623,0.7538476,0.00005312084,0.0003967398,0.2447661,0.0003779783],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000359979,0.00004267013,0.990961,0.0002351969,0.002770292,0.0009452716,0.0005235268,0.0003951721,0.003766948],"genre_scores_gemma":[0.006038932,0.00005022982,0.9832474,0.0002250076,0.0007870661,0.0001575646,0.0009407563,0.00005168792,0.008501337],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7533941,"threshold_uncertainty_score":0.999885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03793855793307345,"score_gpt":0.255987806139043,"score_spread":0.2180492482059696,"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."}}