{"id":"W1994221098","doi":"10.1007/s10209-010-0188-6","title":"BlinkWrite: efficient text entry using eye blinks","year":2010,"lang":"en","type":"article","venue":"Universal Access in the Information Society","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":72,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Text entry; Computer science; Interval (graph theory); Modality (human–computer interaction); Contrast (vision); Character (mathematics); Eye tracking; Speech recognition; Human–computer interaction; Artificial intelligence; Mathematics","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":[0.0003199723,0.001300511,0.001069636,0.001098577,0.0005255605,0.001556429,0.001524043,0.000764008,0.02635202],"category_scores_gemma":[0.001517027,0.000378247,0.0002877767,0.0007563687,0.0002689153,0.001792262,0.001935478,0.0004817369,0.006679509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003042882,"about_ca_system_score_gemma":0.0004698663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002620534,"about_ca_topic_score_gemma":0.004492002,"domain_scores_codex":[0.9996327,0.00003391575,0.00002613501,0.0000911457,0.000169062,0.00004699611],"domain_scores_gemma":[0.9990451,0.0003789938,0.00007790266,0.0001558607,0.0002062748,0.0001358754],"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":[0.003606388,0.0002388601,0.001928197,0.0004911103,0.00007880986,0.0003784627,0.0003304487,0.001489655,0.2039051,0.0008845667,0.03080993,0.7558586],"study_design_scores_gemma":[0.001489848,0.002107476,0.01267522,0.0001653922,0.0002623505,0.00163917,0.0004351558,0.1978935,0.6819224,0.003538184,0.09751709,0.0003542349],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.135118,0.003623725,0.6969643,0.0003653135,0.0007433915,0.0005552472,0.003872005,0.1491247,0.009633321],"genre_scores_gemma":[0.5401751,0.002365904,0.3671225,0.0005562115,0.0005575786,0.0005547769,0.007299234,0.006900305,0.07446823],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02635202,"threshold_uncertainty_score":0.08815628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01696307831476883,"score_gpt":0.284488425815609,"score_spread":0.2675253475008402,"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."}}