{"id":"W1727854234","doi":"10.1007/978-3-540-73214-3_15","title":"Visualization of Uncertainty and Reasoning","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Visualization; Data visualization; Process (computing); Data science; Visual reasoning; Cognition; Analytic reasoning; Information visualization; Visual analytics; Uncertainty analysis; Artificial intelligence; Reasoning system; Psychology; Simulation; Programming language","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001287981,0.001195963,0.000881578,0.002107445,0.0006630443,0.006005107,0.00108422,0.001113381,0.01928268],"category_scores_gemma":[0.006680695,0.0007569221,0.0009628632,0.002030144,0.001265441,0.004967397,0.002384149,0.002225641,0.001959547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009391248,"about_ca_system_score_gemma":0.0009893011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002308657,"about_ca_topic_score_gemma":0.002054665,"domain_scores_codex":[0.9992458,0.0002449676,0.00004102413,0.0001273027,0.0002960802,0.00004491505],"domain_scores_gemma":[0.9976122,0.001579411,0.0001211486,0.0003022859,0.0002866151,0.00009830038],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002315023,0.0000416843,0.0009134441,0.0006472618,0.0001039572,0.0002396443,0.001164164,0.05569487,0.006377978,0.6443509,0.03242199,0.2578126],"study_design_scores_gemma":[0.00001814751,0.00001591138,0.0004548422,0.0001706972,0.00003319752,0.0001617666,0.0002188641,0.1850325,0.004159328,0.7641174,0.04558402,0.00003332268],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008630912,0.005338898,0.9455395,0.002416499,0.0002257949,0.000049822,0.001439561,0.00378006,0.03257896],"genre_scores_gemma":[0.3756343,0.007999144,0.5908748,0.00039671,0.000304842,0.0001703504,0.00306634,0.001334778,0.02021876],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01928268,"threshold_uncertainty_score":0.06450695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02570176872640952,"score_gpt":0.3080985106515642,"score_spread":0.2823967419251547,"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."}}