{"id":"W2766477191","doi":"10.1145/3132525.3132528","title":"BrailleSketch","year":2017,"lang":"en","type":"article","venue":"","topic":"Tactile and Sensory Interactions","field":"Neuroscience","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Braille; Touchscreen; Computer science; Session (web analytics); Gesture; Speech recognition; Word (group theory); Code (set theory); Audio feedback; Text entry; Multimedia; Artificial intelligence; Human–computer interaction; Programming language; 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":[0.0004557227,0.001106824,0.0005574801,0.0008959604,0.0004085454,0.0009518058,0.001609762,0.0008764614,0.04515819],"category_scores_gemma":[0.002463076,0.0003533961,0.0004933583,0.0004801381,0.0003859674,0.001443531,0.001995757,0.000678066,0.01694665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002212277,"about_ca_system_score_gemma":0.0004979472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001369269,"about_ca_topic_score_gemma":0.002805869,"domain_scores_codex":[0.9990844,0.00009324912,0.00006665027,0.0002174924,0.0004377007,0.0001004629],"domain_scores_gemma":[0.9986892,0.0004052985,0.00008455144,0.0002754554,0.0003866557,0.0001588179],"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":[0.001724224,0.0001817057,0.002496304,0.001633709,0.00007176349,0.00112265,0.0008829636,0.0007483735,0.1105418,0.002936706,0.03888019,0.8387796],"study_design_scores_gemma":[0.0004279144,0.002732595,0.02477521,0.001106932,0.0002949615,0.01718153,0.001101109,0.01727311,0.1928829,0.00284316,0.7388928,0.0004877182],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2300652,0.01483384,0.503942,0.001781672,0.004053496,0.001626818,0.005431375,0.0700499,0.1682156],"genre_scores_gemma":[0.5705963,0.003693749,0.2341112,0.001493782,0.0003406356,0.0009376027,0.004205921,0.003675498,0.1809453],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04515819,"threshold_uncertainty_score":0.1510692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0918676795937418,"score_gpt":0.3482327327009143,"score_spread":0.2563650531071725,"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."}}