{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003299525,0.001565253,0.0008586944,0.001543105,0.0007128861,0.002754408,0.00422898,0.001092705,0.01311348],"category_scores_gemma":[0.0117319,0.0009468745,0.001133743,0.00148464,0.0009420329,0.003615951,0.004667909,0.002200413,0.00214829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006141317,"about_ca_system_score_gemma":0.0009738629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003047839,"about_ca_topic_score_gemma":0.004512799,"domain_scores_codex":[0.9979094,0.0005904463,0.0001310646,0.000398867,0.0008300352,0.0001402117],"domain_scores_gemma":[0.9912594,0.003144676,0.000339814,0.003491673,0.001189544,0.0005747584],"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":[0.002020576,0.0006186159,0.00524675,0.001057693,0.0003731901,0.001286335,0.00538319,0.02511968,0.08941695,0.023505,0.07231099,0.7736611],"study_design_scores_gemma":[0.0006363597,0.000894444,0.004957587,0.0003223613,0.0002055879,0.001638828,0.001098697,0.4979198,0.144703,0.02906965,0.3180175,0.0005363084],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01906569,0.0001802506,0.8899736,0.0002745673,0.000152757,0.0002960951,0.0006017633,0.08638498,0.0030703],"genre_scores_gemma":[0.09975765,0.000238246,0.8791186,0.0001795965,0.00004431519,0.0004694804,0.002305448,0.01295001,0.004936616],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01311348,"threshold_uncertainty_score":0.0438689,"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."}}