{"id":"W2020776366","doi":"10.1145/2533682.2533683","title":"Evaluating a Tool for Improving Accessibility to Charts and Graphs","year":2013,"lang":"en","type":"article","venue":"ACM Transactions on Computer-Human Interaction","topic":"Digital Accessibility for Disabilities","field":"Social Sciences","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Comisión Nacional de Investigación Científica y Tecnológica; Natural Sciences and Engineering Research Council of Canada; International Business Machines Corporation","keywords":"Usability; Lexicon; Computer science; Screen reader; Graph; Set (abstract data type); Human–computer interaction; Partially sighted; Artificial intelligence; Visually impaired; Theoretical computer science; Programming language","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":[0.01190065,0.001630366,0.001027942,0.003457055,0.0009853204,0.003615065,0.002526522,0.001890236,0.002363489],"category_scores_gemma":[0.06937579,0.0007070663,0.0007878303,0.001696234,0.001424005,0.004370295,0.002773307,0.001003216,0.0007201185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001259368,"about_ca_system_score_gemma":0.001519207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002654549,"about_ca_topic_score_gemma":0.004276117,"domain_scores_codex":[0.9852558,0.009274579,0.001111398,0.001154601,0.002564268,0.0006392085],"domain_scores_gemma":[0.9255626,0.06045893,0.001917257,0.003838491,0.006906592,0.001316141],"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":[0.003463284,0.005411077,0.0185202,0.005086563,0.0002450286,0.002426359,0.06686664,0.01138556,0.1172105,0.002817701,0.004008849,0.7625583],"study_design_scores_gemma":[0.003735137,0.05721349,0.1297408,0.003027057,0.001956334,0.007827255,0.08808334,0.1891178,0.3549802,0.008718413,0.154199,0.001401167],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8753865,0.0003878305,0.1110715,0.000189394,0.00004262197,0.002335145,0.0003404664,0.0059665,0.00428004],"genre_scores_gemma":[0.631004,0.0003036937,0.3637592,0.00009983774,0.00001565262,0.0009076466,0.0009055235,0.0005657677,0.002438737],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01190065,"threshold_uncertainty_score":0.06293738,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07939364549560197,"score_gpt":0.4096464691966764,"score_spread":0.3302528237010744,"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."}}