{"id":"W2149569288","doi":"10.1145/2559206.2574788","title":"Creating physical visualizations with makervis","year":2014,"lang":"en","type":"preprint","venue":"","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Visualization; Workflow; Data visualization; Analytics; Variety (cybernetics); Human–computer interaction; Construct (python library); Visual analytics; Process (computing); Entertainment; Data science; Database; Artificial intelligence","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":[0.002471526,0.001364353,0.0008812858,0.002863086,0.001266709,0.006109097,0.002193611,0.001542971,0.02595202],"category_scores_gemma":[0.008141033,0.001000402,0.001692042,0.001446926,0.001249506,0.004068018,0.006839565,0.001797702,0.004778988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004009844,"about_ca_system_score_gemma":0.0008939093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005334159,"about_ca_topic_score_gemma":0.0009292287,"domain_scores_codex":[0.9986399,0.0003553071,0.00008975153,0.0002249843,0.0005991421,0.00009084535],"domain_scores_gemma":[0.9963462,0.001930879,0.0001430666,0.0009791312,0.0003694015,0.0002313567],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001327659,0.0004876705,0.006137487,0.00294988,0.0003925203,0.002603124,0.01459559,0.02467813,0.1318031,0.1229205,0.1086535,0.5834507],"study_design_scores_gemma":[0.0002835819,0.0002857981,0.003777515,0.0004458188,0.0001413009,0.001806091,0.002124316,0.1073962,0.1245105,0.08703892,0.6718251,0.0003648372],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.02415391,0.0005160967,0.8917357,0.0007982287,0.0002471741,0.0003130844,0.002059783,0.05578926,0.02438676],"genre_scores_gemma":[0.1296684,0.0006401779,0.8452136,0.0002843183,0.00008533549,0.0005963783,0.002652462,0.008608849,0.01225054],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.02595202,"threshold_uncertainty_score":0.0868181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0263385900812774,"score_gpt":0.3293982502640645,"score_spread":0.3030596601827871,"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."}}