{"id":"W2611136445","doi":"10.1145/3025453.3025520","title":"Investigating Tilt-based Gesture Keyboard Entry for Single-Handed Text Entry on Large Devices","year":2017,"lang":"en","type":"article","venue":"","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Engineering and Physical Sciences Research Council; University of St Andrews","keywords":"Stylus; Text entry; Gesture; Computer science; Tilt (camera); Mobile device; Words per minute; Human–computer interaction; Point (geometry); Touchscreen; Computer vision; Computer graphics (images); Engineering; World Wide Web; 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":[0.001229609,0.0007324206,0.0005798272,0.0003738029,0.0002642756,0.001109611,0.0008256548,0.0006283458,0.003050089],"category_scores_gemma":[0.009578882,0.0002757333,0.0003192943,0.0002886923,0.0004301168,0.00166622,0.0007713035,0.0003546231,0.0007276422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001770731,"about_ca_system_score_gemma":0.0003695662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006952553,"about_ca_topic_score_gemma":0.001040712,"domain_scores_codex":[0.9985045,0.0005847625,0.000128076,0.0001625406,0.0004739617,0.0001461015],"domain_scores_gemma":[0.992792,0.004345284,0.0007844045,0.000492339,0.001264317,0.0003216044],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001931318,0.0009510896,0.01504923,0.00162224,0.00006314417,0.0009072783,0.003254975,0.002880037,0.7229671,0.0008346072,0.0007791598,0.2487597],"study_design_scores_gemma":[0.000698906,0.04067609,0.1716027,0.0005389518,0.0004900043,0.00956335,0.004718309,0.1342317,0.618827,0.001386352,0.01676514,0.0005016043],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9140617,0.0005400222,0.08213257,0.00006874804,0.00003790452,0.0003896882,0.00006136671,0.0005289014,0.002179138],"genre_scores_gemma":[0.9305279,0.0003531867,0.06697439,0.00003840419,0.00001945916,0.0001697458,0.0000859287,0.00006713087,0.001763693],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003050089,"threshold_uncertainty_score":0.01020354,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03459027300470446,"score_gpt":0.2955148884860079,"score_spread":0.2609246154813034,"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."}}