{"id":"W2054160847","doi":"10.1177/1473871611413099","title":"Information visualization evaluation in large companies: Challenges, experiences and recommendations","year":2011,"lang":"en","type":"article","venue":"Information Visualization","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Visualization; Computer science; Information visualization; Set (abstract data type); Data science; Plan (archaeology); Context (archaeology); Work (physics); Focus (optics); Creative visualization; Data visualization; Automotive industry; Knowledge management; Data mining; Engineering","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":[0.1612379,0.001425367,0.001631408,0.003049219,0.004312489,0.01559719,0.004929363,0.003818626,0.002918713],"category_scores_gemma":[0.2885357,0.0008730428,0.0009081694,0.004252688,0.003101598,0.02057816,0.005132662,0.003370498,0.0009069956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005579073,"about_ca_system_score_gemma":0.005769585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01018157,"about_ca_topic_score_gemma":0.01556744,"domain_scores_codex":[0.8247777,0.1397079,0.007636819,0.004105739,0.02000905,0.003762824],"domain_scores_gemma":[0.5431642,0.3421529,0.0123294,0.0198459,0.07251889,0.009988707],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0008721306,0.003233172,0.04878556,0.005534211,0.0003108397,0.00192303,0.1161932,0.00677915,0.006154929,0.008772881,0.06920855,0.7322325],"study_design_scores_gemma":[0.000904577,0.004140669,0.05431427,0.01075958,0.0005697403,0.003492641,0.573597,0.05773569,0.02802397,0.05444134,0.2106983,0.001322228],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6293116,0.01949979,0.1391527,0.1756369,0.001237581,0.003240543,0.000817503,0.002940665,0.02816276],"genre_scores_gemma":[0.7751405,0.007023731,0.2064128,0.004136513,0.000288126,0.002117675,0.0009138173,0.000583928,0.003382797],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1612379,"threshold_uncertainty_score":0.8527178,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07048500596307443,"score_gpt":0.3476712538339281,"score_spread":0.2771862478708537,"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."}}