{"id":"W2528099986","doi":"10.1109/dasc-picom-datacom-cyberscitec.2016.148","title":"A Data Science Model for Big Data Analytics of Frequent Patterns","year":2016,"lang":"en","type":"article","venue":"","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Big data; Computer science; Data science; Data mining; Variety (cybernetics); Tree (set theory); Task (project management); Analytics; Artificial intelligence","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":["open_science"],"consensus_categories":[],"category_scores_codex":[0.0006583964,0.00006252126,0.00008703989,0.00007171412,0.00008198984,0.0000863902,0.008435399,0.0000144725,0.000002363376],"category_scores_gemma":[0.0001139631,0.00003875389,0.00001084745,0.0002997321,0.0001188697,0.00142718,0.003730499,0.00001885289,0.000006569328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001382726,"about_ca_system_score_gemma":0.0002589196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005199408,"about_ca_topic_score_gemma":0.0000597806,"domain_scores_codex":[0.9987375,0.000003674766,0.0001881586,0.0006420948,0.0002374023,0.0001911595],"domain_scores_gemma":[0.9943482,0.00008419239,0.00007579265,0.005309203,0.0001091476,0.00007347709],"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":[8.612544e-7,0.0001080316,0.0002375387,0.00001254339,0.00001212492,2.954592e-7,0.00005554881,0.00004535445,0.003656084,0.09919073,0.01175903,0.8849218],"study_design_scores_gemma":[0.0001169302,0.00001122513,0.0001423512,0.00001465094,0.000005749663,9.842013e-7,0.000005790855,0.9918017,0.0008442416,0.001627744,0.00535675,0.00007186501],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005397895,0.000006237513,0.9923015,0.001487995,0.00009821065,0.0001050141,0.005231277,0.00003783151,0.0001921251],"genre_scores_gemma":[0.2393622,0.00002840374,0.7598459,0.0001385258,0.00007102769,0.00001049754,0.0001526166,0.000004976529,0.0003858171],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9917564,"threshold_uncertainty_score":0.9969295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3168023449115383,"score_gpt":0.3607614895957989,"score_spread":0.0439591446842606,"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."}}