{"id":"W2153836330","doi":"10.1109/tvcg.2007.70436","title":"Visual Perception and Mixed-Initiative Interaction for Assisted Visualization Design","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Waterloo; National Science Foundation","keywords":"Computer science; Visualization; Human–computer interaction; Context (archaeology); Salient; Perception; Data visualization; Creative visualization; Domain (mathematical analysis); Data science; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003537493,0.001263109,0.0004214321,0.0008179523,0.0006307687,0.002633019,0.002189217,0.001158017,0.004738858],"category_scores_gemma":[0.0134,0.0006050873,0.0006958221,0.000324352,0.001449119,0.002654602,0.002656186,0.001057449,0.0007741791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005410611,"about_ca_system_score_gemma":0.0006743987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008229465,"about_ca_topic_score_gemma":0.001204393,"domain_scores_codex":[0.9973417,0.001822309,0.0001101302,0.0002666033,0.0003521849,0.0001071727],"domain_scores_gemma":[0.9946101,0.003747077,0.0002643934,0.0005405901,0.0005261055,0.0003118222],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001284868,0.0006574803,0.003895379,0.002220083,0.0002689551,0.001088921,0.02062394,0.08502927,0.1357259,0.1434787,0.009811841,0.5959145],"study_design_scores_gemma":[0.0005026213,0.001512226,0.00277684,0.0004337187,0.0001875942,0.001215723,0.002782211,0.6742891,0.05165897,0.1774286,0.08691952,0.0002928235],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01208562,0.0001664071,0.983609,0.0002222672,0.00001914811,0.00014707,0.00001867725,0.001075628,0.00265629],"genre_scores_gemma":[0.2478598,0.0001686971,0.7497,0.0001304342,0.00001850135,0.0006180663,0.00006943555,0.0002337707,0.001201315],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004738858,"threshold_uncertainty_score":0.01870829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06620721598083383,"score_gpt":0.3252732925827256,"score_spread":0.2590660766018917,"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."}}