{"id":"W3199977262","doi":"10.1109/mcg.2021.3102711","title":"Powering Visualization With Deep Learning","year":2021,"lang":"en","type":"article","venue":"IEEE Computer Graphics and Applications","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Visualization; Computer science; Deep learning; Visual analytics; Leverage (statistics); Data visualization; Artificial intelligence; Human–computer interaction; Data science; Focus (optics); Information visualization; Creative visualization; Learning analytics","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":[0.001740709,0.001157428,0.0006438129,0.002224377,0.0006971044,0.00491049,0.001118684,0.001335363,0.01996017],"category_scores_gemma":[0.01081331,0.0005767665,0.0007759487,0.001670796,0.0009235325,0.005387451,0.002620697,0.003404191,0.00487564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000628309,"about_ca_system_score_gemma":0.0006898022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008083282,"about_ca_topic_score_gemma":0.001956025,"domain_scores_codex":[0.9991137,0.0002149432,0.00005522019,0.0001269507,0.0004237555,0.0000653713],"domain_scores_gemma":[0.9952123,0.002523655,0.0001887925,0.0005188377,0.001295889,0.0002605081],"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":[0.0001117049,0.00005870288,0.0007742557,0.0007107836,0.0001227212,0.0001772418,0.0003245468,0.01363918,0.006966923,0.06473549,0.2906245,0.6217539],"study_design_scores_gemma":[0.00006117351,0.00007224958,0.0005799859,0.0005124413,0.00008378398,0.0003112362,0.0001812955,0.1635739,0.01391058,0.1615384,0.6590872,0.00008776534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006804741,0.02665764,0.8854766,0.02215044,0.01555491,0.0001272636,0.0007771499,0.01307147,0.02937982],"genre_scores_gemma":[0.1508101,0.06091461,0.6676985,0.007371868,0.02729548,0.0004953022,0.003357906,0.01094405,0.0711121],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01996017,"threshold_uncertainty_score":0.06677341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01264176593625232,"score_gpt":0.2693196983516385,"score_spread":0.2566779324153862,"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."}}