{"id":"W3080485811","doi":"10.1109/tvcg.2020.3018724","title":"ChartSeer: Interactive Steering Exploratory Visual Analysis With Machine Intelligence","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Visualization; Baseline (sea); Intelligence analysis; Data science; Human–computer interaction; Data visualization; Exploratory data analysis; Session (web analytics); Asynchronous communication; Artificial intelligence; Machine learning; Data mining; World Wide Web","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":[0.002803548,0.002325037,0.0008448191,0.002794355,0.0005449168,0.002901505,0.002787229,0.001099417,0.01464052],"category_scores_gemma":[0.01247555,0.0009516026,0.001417679,0.0009997445,0.0008592297,0.003210515,0.005393918,0.001657583,0.003967647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000594909,"about_ca_system_score_gemma":0.001473377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00391226,"about_ca_topic_score_gemma":0.005806214,"domain_scores_codex":[0.9983571,0.0004934203,0.0001011433,0.0003833926,0.0005344189,0.0001304972],"domain_scores_gemma":[0.9946263,0.003226528,0.000339493,0.0006723174,0.0007491536,0.0003861708],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002345234,0.0004935556,0.005684617,0.001730386,0.000426053,0.001019156,0.004538207,0.04081665,0.06259472,0.01409845,0.1091088,0.7571442],"study_design_scores_gemma":[0.0004422678,0.0005913278,0.005200202,0.0005213741,0.0001617747,0.0006358964,0.001187544,0.7425265,0.06027182,0.04744758,0.1405316,0.0004821058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0115358,0.0004180951,0.8845576,0.000341247,0.0001227712,0.0005390996,0.001806383,0.09615333,0.004525603],"genre_scores_gemma":[0.134566,0.0005909333,0.8472285,0.0004177846,0.00008918737,0.001017038,0.004042829,0.006239377,0.005808367],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01464052,"threshold_uncertainty_score":0.04897738,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02282986073845864,"score_gpt":0.2795975151436216,"score_spread":0.2567676544051629,"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."}}