{"id":"W2976983092","doi":"10.1109/iccse.2019.8845345","title":"Proposing a Pareto-VIKOR Ranking Method for Enhancing Parallel Coordinates Visualization","year":2019,"lang":"en","type":"article","venue":"","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Visualization; Computer science; Parallel coordinates; Metric (unit); Ranking (information retrieval); Plot (graphics); Sorting; Pairwise comparison; Data mining; Pareto principle; Multi-objective optimization; Data visualization; Contour line; Mathematical optimization; Algorithm; Artificial intelligence; Mathematics; Machine learning; Statistics","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":[0.00206504,0.002429626,0.001883303,0.006953298,0.001261405,0.003114495,0.001734251,0.001319093,0.004463588],"category_scores_gemma":[0.004774928,0.0006619428,0.001708472,0.004615746,0.0006369238,0.002425917,0.001753725,0.001253707,0.001593289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001153171,"about_ca_system_score_gemma":0.002650419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007761271,"about_ca_topic_score_gemma":0.00691868,"domain_scores_codex":[0.9977868,0.0004511512,0.0001416305,0.0002784471,0.001131633,0.0002103343],"domain_scores_gemma":[0.9981103,0.0004991741,0.0001827797,0.0001525978,0.0009518767,0.0001033475],"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":[0.0001795601,0.0001789774,0.002307235,0.0004971288,0.0001511759,0.0003064968,0.0003594673,0.3037625,0.01750588,0.02361234,0.01113107,0.6400082],"study_design_scores_gemma":[0.00002590938,0.00009652814,0.0006585985,0.0000322816,0.00002671076,0.0001494187,0.0001141302,0.9803329,0.005497771,0.007768783,0.005233645,0.0000632563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008988599,0.00030611,0.9860808,0.0001669509,0.00006873954,0.0001073996,0.0001351107,0.001133253,0.003013027],"genre_scores_gemma":[0.1649549,0.0005717781,0.8285357,0.0001255307,0.0000873497,0.000363236,0.0006093463,0.0004252698,0.004327086],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007761271,"threshold_uncertainty_score":0.01543218,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01842503554552514,"score_gpt":0.3391844588951215,"score_spread":0.3207594233495963,"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."}}