{"id":"W4402721926","doi":"10.1145/3670947.3670977","title":"Investigating User Estimation of Missing Data in Visual Analysis","year":2024,"lang":"en","type":"article","venue":"Graphics Interface","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Universitas Brawijaya","keywords":"Computer science; Estimation; Missing data; Artificial intelligence; Data mining; Machine learning; 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":[0.04933211,0.001564538,0.001321653,0.002929961,0.001699803,0.005909828,0.002405057,0.002283473,0.006279298],"category_scores_gemma":[0.3644062,0.0007581966,0.001513681,0.001605354,0.002400603,0.005367212,0.004816202,0.001958086,0.001215709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001552722,"about_ca_system_score_gemma":0.001475777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004096984,"about_ca_topic_score_gemma":0.00327438,"domain_scores_codex":[0.9533523,0.03518503,0.002036491,0.003505996,0.005065543,0.0008546042],"domain_scores_gemma":[0.5012357,0.4396879,0.01280857,0.02482315,0.01984005,0.001604675],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.01239425,0.001162013,0.1976873,0.008128907,0.001199616,0.002047323,0.171,0.04793634,0.03416142,0.01924125,0.02563861,0.479403],"study_design_scores_gemma":[0.001052986,0.003049522,0.1289144,0.004564594,0.001197548,0.002458901,0.06441476,0.6234119,0.03477432,0.07870115,0.05588908,0.001570858],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6426452,0.001262023,0.3356425,0.002106938,0.0003189506,0.0008935949,0.001481945,0.004998266,0.0106506],"genre_scores_gemma":[0.8983155,0.00023383,0.09786661,0.0004676005,0.00006654362,0.0005813511,0.0006730565,0.000660333,0.001135214],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04933211,"threshold_uncertainty_score":0.2608963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06049592183069265,"score_gpt":0.3864893038071454,"score_spread":0.3259933819764528,"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."}}