{"id":"W2167736126","doi":"10.1109/mcg.2015.40","title":"Preparing Undergraduates for Visual Analytics","year":2015,"lang":"en","type":"article","venue":"IEEE Computer Graphics and Applications","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Visual analytics; Computer science; Analytics; Data science; Visualization; Component (thermodynamics); Computer graphics; Cognition; Human–computer interaction; Artificial intelligence; Psychology","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":[0.003676393,0.001155317,0.0007048603,0.001976053,0.003349075,0.007165369,0.002154608,0.001741699,0.0724685],"category_scores_gemma":[0.01368313,0.0008686574,0.0009098953,0.001274401,0.001081803,0.00314655,0.006110571,0.003771909,0.03240323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002343137,"about_ca_system_score_gemma":0.006746888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001776835,"about_ca_topic_score_gemma":0.005266552,"domain_scores_codex":[0.9979349,0.0002792027,0.0001356792,0.0002997603,0.0006581704,0.0006921575],"domain_scores_gemma":[0.983125,0.0009579163,0.0007452494,0.001112115,0.004263207,0.009796544],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003841091,0.004448894,0.01346942,0.0003773614,0.0000189554,0.001068022,0.007563171,0.0007071433,0.007585155,0.008318211,0.6158372,0.3402225],"study_design_scores_gemma":[0.0001368352,0.001840836,0.02290112,0.0006667641,0.00003229854,0.001213741,0.02203271,0.002852528,0.009474957,0.0251115,0.9136077,0.0001288745],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5132618,0.00435135,0.0639934,0.08273199,0.01283212,0.006297433,0.007643983,0.01539248,0.2934954],"genre_scores_gemma":[0.55518,0.003814889,0.1416167,0.02213729,0.001504053,0.003194474,0.009212934,0.001522511,0.2618172],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0724685,"threshold_uncertainty_score":0.2424312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04897367556961103,"score_gpt":0.3289652691882265,"score_spread":0.2799915936186155,"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."}}