{"id":"W2609731728","doi":"10.1109/access.2017.2696365","title":"Machine Learning With Big Data: Challenges and Approaches","year":2017,"lang":"en","type":"article","venue":"IEEE Access","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1022,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Big data; Computer science; Data science; Artificial intelligence; Machine learning; Context (archaeology); Variety (cybernetics); Field (mathematics); Process (computing); Domain (mathematical analysis); Grand Challenges; Set (abstract data type); Data mining","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":[0.0001313422,0.00006617471,0.00007292853,0.00002999028,0.0004332111,0.0005191361,0.001823324,0.00002653622,6.892549e-7],"category_scores_gemma":[0.000008353911,0.00005033088,0.00000686581,0.00003276956,0.00006269486,0.0009854877,0.0006488181,0.00009980653,0.000002754254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003551781,"about_ca_system_score_gemma":0.00001022985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008148922,"about_ca_topic_score_gemma":0.0000910068,"domain_scores_codex":[0.9994412,0.00001233431,0.00005801384,0.0003187659,0.00007689354,0.00009280398],"domain_scores_gemma":[0.9987119,0.00001658027,0.00009554168,0.001121558,0.00001641945,0.00003804547],"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":[0.000001842812,0.00001695225,0.001392141,0.00001124872,0.000008066855,0.000002112055,0.00008153314,0.000008828647,0.00003277575,0.009304626,0.00004914861,0.9890907],"study_design_scores_gemma":[0.001383175,0.0005265417,0.2505576,0.0001330198,0.00005916715,0.0002393664,0.0001379344,0.3998967,0.02322577,0.02120154,0.3011349,0.001504273],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01024926,0.001135431,0.9758743,0.002599366,0.00008822647,0.0001447979,0.000002914641,0.0002775511,0.009628154],"genre_scores_gemma":[0.9919069,0.0008665512,0.006892532,0.00002903738,0.00009733059,0.00002718772,0.000001516176,0.000005623161,0.0001732804],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9875864,"threshold_uncertainty_score":0.5006042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3055868627429184,"score_gpt":0.3294927761983931,"score_spread":0.02390591345547471,"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."}}