{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001251622,0.000271241,0.0003063344,0.0005964015,0.0002576227,0.0003905738,0.0003707899,0.00007113541,0.00003305906],"category_scores_gemma":[0.0000030979,0.000249188,0.00009867521,0.003231069,0.00008236815,0.0008662764,0.00001465077,0.0002176703,0.00001598624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002033259,"about_ca_system_score_gemma":0.00004052023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009271408,"about_ca_topic_score_gemma":0.00002669266,"domain_scores_codex":[0.9982956,0.0001259877,0.0003595979,0.0006122825,0.0003923559,0.0002141725],"domain_scores_gemma":[0.9990278,0.00008020569,0.0001401278,0.0002795818,0.0002002769,0.0002719366],"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":[0.000218001,0.001253449,0.0008625254,0.0001869686,0.002446172,0.00006177965,0.01875948,0.06383196,0.0000724835,0.8579225,0.0002953924,0.05408927],"study_design_scores_gemma":[0.0002614345,0.0005124361,0.00008918187,0.00002650934,0.0001417432,0.000005191507,0.0001385731,0.9954355,0.002170034,0.00004915578,0.0008636932,0.0003065864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001579072,0.00002784735,0.9974671,0.0002052923,0.0001651363,0.0001500748,0.00002102362,0.0003591935,0.00002527191],"genre_scores_gemma":[0.9931601,0.0002128659,0.001911288,0.004581638,0.00004401945,0.00001405562,0.00003382959,0.00002292666,0.00001923688],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9955558,"threshold_uncertainty_score":0.9999961,"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."}}