{"id":"W3112172908","doi":"10.1109/smc42975.2020.9282852","title":"Enhancing Parallel Coordinates Visualization Using Genetic Algorithm with Smart Mutation","year":2020,"lang":"en","type":"article","venue":"","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Parallel coordinates; Visualization; Intersection (aeronautics); Computer science; Metric (unit); Algorithm; Face (sociological concept); Mutation; Similarity (geometry); Genetic algorithm; Operator (biology); Data visualization; Data mining; Artificial intelligence; Machine learning; Image (mathematics)","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":[],"consensus_categories":[],"category_scores_codex":[0.00006708689,0.0001043688,0.0001071165,0.0000588079,0.0001019855,0.0002408789,0.0002478923,0.0000282137,0.00003869991],"category_scores_gemma":[0.0000271395,0.00008912048,0.00001925941,0.0006629729,0.00001906599,0.0005341737,0.00008264907,0.00003526847,0.00003674855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001887772,"about_ca_system_score_gemma":0.00006179867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002862983,"about_ca_topic_score_gemma":0.000006102703,"domain_scores_codex":[0.9991049,0.00004056503,0.0001978905,0.0002827979,0.0002247519,0.0001490828],"domain_scores_gemma":[0.999512,0.00002051538,0.00008543166,0.0001513678,0.0001249491,0.0001057565],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005292254,0.0006537685,0.01082131,0.000475558,0.0004021602,0.0004533836,0.01582099,0.2694063,0.01781385,0.4435537,0.005202413,0.2353437],"study_design_scores_gemma":[0.0002525151,0.00008140135,0.0001984029,0.00001297964,0.0000104742,0.00001238132,0.0000998008,0.9936725,0.00474102,0.0001710839,0.0006016929,0.0001457333],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001871043,0.00002053033,0.9972249,0.0003532318,0.00004180406,0.0000932339,0.000001650272,0.0002001377,0.000193422],"genre_scores_gemma":[0.1652367,0.000009162967,0.8321224,0.002392751,0.00006020297,0.000002810351,0.0000434752,0.00001523534,0.0001172987],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7242662,"threshold_uncertainty_score":0.3634228,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02566861940022008,"score_gpt":0.2885592408431707,"score_spread":0.2628906214429506,"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."}}