{"id":"W2280565273","doi":"10.1007/978-3-642-40131-2_32","title":"MAF: A Method for Detecting Temporal Associations from Multiple Event Sequences","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Univariate; Computer science; Multivariate statistics; Event (particle physics); Data mining; Set (abstract data type); Multivariate analysis; Artificial intelligence; Pattern recognition (psychology); Machine learning","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":[0.003224217,0.002139235,0.001858399,0.008002665,0.001025856,0.001679257,0.00281635,0.002274693,0.005909141],"category_scores_gemma":[0.01163815,0.0009232875,0.001972449,0.004735358,0.0005648655,0.002603352,0.001538971,0.001941868,0.003825023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003697879,"about_ca_system_score_gemma":0.001203034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004869111,"about_ca_topic_score_gemma":0.005405328,"domain_scores_codex":[0.99812,0.0003177838,0.000190495,0.0005648783,0.0006565896,0.0001501952],"domain_scores_gemma":[0.9945426,0.003514198,0.0005275151,0.0005750872,0.0006712326,0.0001693735],"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":[0.0005910469,0.0001825916,0.007615165,0.0003715045,0.0004295613,0.000561343,0.0002459976,0.0162069,0.02616097,0.004745137,0.01716072,0.9257291],"study_design_scores_gemma":[0.000116854,0.0002627796,0.01159604,0.000147515,0.0003393274,0.002572577,0.0001991431,0.8811861,0.03011861,0.03092957,0.04227633,0.0002551737],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004132933,0.0004581942,0.9884399,0.00005360484,0.00009898254,0.00007751533,0.0009632589,0.005435605,0.0003398998],"genre_scores_gemma":[0.05042534,0.0004065149,0.9425595,0.00009702699,0.0002032659,0.0003027914,0.002805156,0.000727117,0.002473322],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008002665,"threshold_uncertainty_score":0.01976806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03069293355381551,"score_gpt":0.2759551487512473,"score_spread":0.2452622151974318,"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."}}