{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001231142,0.000213186,0.0002591999,0.0001101483,0.000121739,0.0006561904,0.0009937886,0.0000795176,0.00007537805],"category_scores_gemma":[0.00004342146,0.0001633973,0.00006803796,0.0003187281,0.0000387898,0.0001671796,0.001280806,0.000189695,0.0001372439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002360939,"about_ca_system_score_gemma":0.000126925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003270877,"about_ca_topic_score_gemma":0.00001351559,"domain_scores_codex":[0.9986454,0.00006698713,0.0002012086,0.0005371629,0.0003565407,0.0001927384],"domain_scores_gemma":[0.9985324,0.00006502971,0.0001609014,0.0009542531,0.0001806293,0.0001067422],"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":[8.856372e-7,0.00014953,0.000306383,0.00008889193,0.00005041904,0.000004161589,0.0006545704,0.008127933,0.000009700793,0.9824709,0.005207005,0.002929668],"study_design_scores_gemma":[0.0001185162,0.00003483799,0.00009351585,0.00008331928,0.00002172906,0.000002925608,0.00002036704,0.9879852,0.0001439049,0.004336357,0.00688667,0.0002726925],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007089213,0.000005434648,0.96852,0.0003855703,0.00009624718,0.0001154186,0.00001003591,0.0004251224,0.02973325],"genre_scores_gemma":[0.7870352,0.00002301643,0.2000045,0.00327031,0.0006140001,0.0000489294,0.0006202518,0.00006339732,0.008320417],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9798572,"threshold_uncertainty_score":0.6663148,"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."}}