{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0002458501,0.0004129006,0.0004648906,0.0003117936,0.0002270493,0.001346706,0.001527684,0.0001571776,0.00005878604],"category_scores_gemma":[0.0001420336,0.0003374226,0.0001466657,0.0008767822,0.00003466231,0.001429971,0.002693651,0.0003946731,0.00008637576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001399644,"about_ca_system_score_gemma":0.0002819496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002360646,"about_ca_topic_score_gemma":0.0004788657,"domain_scores_codex":[0.9975718,0.00007862621,0.0004714211,0.0009917925,0.0004340508,0.0004523003],"domain_scores_gemma":[0.9981704,0.0002206076,0.0003793777,0.0007335487,0.0003936348,0.0001024637],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001015753,0.008193776,0.01135365,0.01041903,0.008839579,0.0004232175,0.3722258,0.0959289,0.005830226,0.170542,0.2639668,0.05126135],"study_design_scores_gemma":[0.000781535,0.0001859892,0.0001707506,0.0004561645,0.00006800634,0.00001076675,0.003511097,0.9847676,0.006018924,0.0009309006,0.002386353,0.0007118892],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004676491,0.00006580483,0.991074,0.0005308879,0.001211526,0.0008099297,0.0001375616,0.0009372844,0.0005565241],"genre_scores_gemma":[0.9169771,0.0001388096,0.06804764,0.0009402821,0.0002756809,0.0003140129,0.002067787,0.0001276321,0.01111106],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9230263,"threshold_uncertainty_score":0.9999078,"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."}}