{"id":"W2965831557","doi":"10.4018/ijamc.2019100102","title":"Solving Heterogeneous Big Data Mining Problems Using Multi-Objective Optimization","year":2019,"lang":"en","type":"article","venue":"International Journal of Applied Metaheuristic Computing","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Big data; Data processing; Data mining; Artificial intelligence; Database","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"],"consensus_categories":[],"category_scores_codex":[0.002263952,0.0002869914,0.0005230745,0.0007859493,0.0001466424,0.0006601484,0.004091306,0.00008888636,0.00006186297],"category_scores_gemma":[0.0007195374,0.0002771986,0.000124679,0.000601574,0.00005907406,0.0005936318,0.001995918,0.0004277403,0.00002345342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002477692,"about_ca_system_score_gemma":0.0004434214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001468084,"about_ca_topic_score_gemma":0.000001200906,"domain_scores_codex":[0.9958254,0.0001574256,0.001304014,0.0006703593,0.001615111,0.0004276638],"domain_scores_gemma":[0.9953031,0.0006860323,0.00142716,0.0008373196,0.001555572,0.000190806],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003081014,0.0001260112,0.0002365334,0.00002194863,0.0003352026,0.00007159534,0.0005717159,0.9490556,0.0008589809,0.0003796564,0.00002411363,0.04828779],"study_design_scores_gemma":[0.001313738,0.00005456649,0.00009074294,0.0001341256,0.00004301937,0.0004159712,0.0000951495,0.9968085,0.0004078762,0.000143507,0.0002341809,0.0002586402],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01029638,0.0001743097,0.9844577,0.00009008912,0.003910321,0.0003379696,0.000009636056,0.00006090727,0.0006626954],"genre_scores_gemma":[0.4369728,0.00002099703,0.5624065,0.00007489624,0.0004623918,8.723542e-7,0.0000126764,0.00002522748,0.00002358783],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4266764,"threshold_uncertainty_score":0.999968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08629247167844621,"score_gpt":0.3261662510747974,"score_spread":0.2398737793963512,"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."}}