{"id":"W2149931362","doi":"10.1109/mcg.2009.78","title":"CoCoNutTrix: Collaborative Retrofitting for Information Visualization","year":2009,"lang":"en","type":"article","venue":"IEEE Computer Graphics and Applications","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Petro-Canada","funders":"","keywords":"Computer science; Visualization; Retrofitting; Data visualization; Information visualization; Domain (mathematical analysis); Data science; Social network analysis; Human–computer interaction; World Wide Web; Data mining; Engineering; Social media","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":[0.0001959799,0.0001097254,0.0001188464,0.0002157229,0.0003177106,0.0004764914,0.0003011845,0.0000580653,4.637142e-7],"category_scores_gemma":[0.000009179849,0.00011157,0.00003611601,0.0009058739,0.00003326132,0.0009406146,0.00003587694,0.00005080965,0.000005798022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001083901,"about_ca_system_score_gemma":0.00004035393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001502746,"about_ca_topic_score_gemma":9.46745e-7,"domain_scores_codex":[0.9992034,0.00001651138,0.0002834407,0.0002155608,0.0001380038,0.0001430457],"domain_scores_gemma":[0.999029,0.00006712121,0.0001687608,0.0002703607,0.000397872,0.0000668685],"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":[0.000001033368,0.00002763189,0.00004296858,0.000009566093,0.000005489684,4.707104e-8,0.000124032,0.00008169853,0.0000270254,0.9523309,0.002197449,0.04515219],"study_design_scores_gemma":[0.0003798257,0.00009106043,0.0006967097,0.00001029794,0.00000955927,0.000002368866,0.00001658192,0.7905373,0.0002134791,0.03291894,0.1749476,0.0001763493],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0002655162,0.00002609824,0.9979763,0.0006889717,0.00008814081,0.0005486438,0.00003360731,0.0001577415,0.0002150013],"genre_scores_gemma":[0.7807342,0.0004665757,0.1956306,0.02076833,0.001012579,0.0004237443,0.0008887568,0.00002113091,0.00005401455],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.919412,"threshold_uncertainty_score":0.4594817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01390484854425042,"score_gpt":0.2959758157368013,"score_spread":0.2820709671925509,"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."}}