{"id":"W4321175654","doi":"10.1145/3581641.3584099","title":"SeeChart: Enabling Accessible Visualizations Through Interactive Natural Language Interface For People with Visual Impairments","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Computer science; Reading (process); Visualization; World Wide Web; Publication; Data visualization; Chart; Interface (matter); Human–computer interaction; User interface; Newspaper; Multimedia; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00243295,0.001384104,0.0004615223,0.001277825,0.0004119058,0.001655768,0.00124111,0.001058823,0.02042372],"category_scores_gemma":[0.01647313,0.0003716374,0.0006905978,0.0005056898,0.0007481111,0.003808451,0.003693901,0.0007596405,0.003503082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002907257,"about_ca_system_score_gemma":0.0008008639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001081967,"about_ca_topic_score_gemma":0.002066795,"domain_scores_codex":[0.9989694,0.0004569714,0.00009766232,0.0001644783,0.0002152838,0.00009618352],"domain_scores_gemma":[0.9904772,0.00735552,0.0003411579,0.0007375834,0.0007251196,0.0003634323],"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":[0.003154338,0.0007743385,0.01295317,0.004924603,0.0001402996,0.005713332,0.04283437,0.002704625,0.08690134,0.01074603,0.1506734,0.6784801],"study_design_scores_gemma":[0.001507352,0.002630359,0.03749445,0.004202824,0.0005365433,0.01044444,0.02135003,0.05996633,0.1032788,0.06991045,0.687636,0.001042503],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1852291,0.001326451,0.6583598,0.002000693,0.0004836711,0.001563099,0.009009929,0.1234705,0.01855675],"genre_scores_gemma":[0.4776821,0.001489148,0.4899242,0.0009218042,0.0001530047,0.002852516,0.00635185,0.005078082,0.01554721],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02042372,"threshold_uncertainty_score":0.06832409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03203773097157847,"score_gpt":0.3949505785432047,"score_spread":0.3629128475716262,"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."}}