{"id":"W2059136675","doi":"10.1145/1168987.1168992","title":"Indirect text entry using one or two keys","year":2006,"lang":"en","type":"article","venue":"","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Huffman coding; Hierarchy; Text entry; Containment (computer programming); Coding (social sciences); Key (lock); Encoding (memory); Theoretical computer science; Artificial intelligence; Programming language; Human–computer interaction; Computer security; Mathematics; Statistics","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":[0.001630206,0.001013778,0.0005844858,0.0007770883,0.0005756253,0.002529586,0.002049996,0.001010356,0.02126385],"category_scores_gemma":[0.01375792,0.000440308,0.0005272457,0.0007906076,0.001197294,0.006407299,0.002778517,0.001235229,0.00446262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005338017,"about_ca_system_score_gemma":0.000926797,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007676745,"about_ca_topic_score_gemma":0.0007350371,"domain_scores_codex":[0.9977844,0.0005337229,0.0002135965,0.0004053768,0.0008992262,0.0001635846],"domain_scores_gemma":[0.9889959,0.006006193,0.0007048758,0.003075555,0.001009274,0.0002080976],"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":[0.002500531,0.0004169976,0.007286567,0.001988382,0.00009273602,0.001253161,0.004871123,0.02211289,0.1280331,0.2457417,0.009413736,0.5762891],"study_design_scores_gemma":[0.0004116025,0.002277235,0.007419595,0.0006744993,0.000267079,0.004591398,0.001999742,0.4202998,0.2565141,0.1294259,0.1756673,0.0004517874],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07218833,0.0002914819,0.9029156,0.0001973714,0.00007508983,0.0003338945,0.0005688529,0.006463328,0.01696599],"genre_scores_gemma":[0.6456404,0.0003332439,0.331649,0.0001810482,0.00005312432,0.0004794197,0.001078533,0.001110654,0.01947458],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02126385,"threshold_uncertainty_score":0.07113463,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02969157385854818,"score_gpt":0.2839925466615669,"score_spread":0.2543009728030187,"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."}}