{"id":"W2029143245","doi":"10.1109/tvcg.2013.160","title":"GPLOM: The Generalized Plot Matrix for Visualizing Multidimensional Multivariate Data","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"Systems, Applications & Products in Data Processing (Canada); École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Categorical variable; Computer science; Multivariate statistics; Plot (graphics); Data Matrix; Variable (mathematics); Matrix (chemical analysis); Visualization; Continuous variable; Design matrix; Data mining; Data visualization; Parallel coordinates; Feature (linguistics); Artificial intelligence; Pattern recognition (psychology); Mathematics; Statistics; Regression analysis; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004460609,0.002776524,0.001429494,0.004817606,0.0009739528,0.004434138,0.003488168,0.001510459,0.04379054],"category_scores_gemma":[0.01866858,0.001145251,0.002093887,0.005693517,0.0009225831,0.006450904,0.004492853,0.002625309,0.01150812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006862535,"about_ca_system_score_gemma":0.001832831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003865137,"about_ca_topic_score_gemma":0.005030132,"domain_scores_codex":[0.9972489,0.001031379,0.0002644303,0.0004005171,0.0009195979,0.0001352441],"domain_scores_gemma":[0.99018,0.005651191,0.0007042974,0.001344207,0.001789011,0.0003312055],"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":[0.000797964,0.0002046764,0.004085924,0.003665125,0.0004151814,0.0009122025,0.003313736,0.01401575,0.0160059,0.04948815,0.35658,0.5505154],"study_design_scores_gemma":[0.0005901644,0.0004531635,0.007927682,0.001009411,0.0002416909,0.001970061,0.001240992,0.2743775,0.02589397,0.1613249,0.5242983,0.0006721781],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002453882,0.0004501649,0.9428146,0.0004481679,0.0001585187,0.000350099,0.006936093,0.04375907,0.002629509],"genre_scores_gemma":[0.02363592,0.0007904597,0.9548256,0.0003343486,0.00009880073,0.001156986,0.008370501,0.00776238,0.003024936],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04379054,"threshold_uncertainty_score":0.146494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05670578463631569,"score_gpt":0.3408862254024929,"score_spread":0.2841804407661772,"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."}}