{"id":"W2154379878","doi":"10.1145/1979742.1979799","title":"Gathering text entry metrics on android devices","year":2011,"lang":"en","type":"article","venue":"","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Handwriting; Text entry; Computer science; Android (operating system); Mobile device; Handwriting recognition; Speech recognition; Typing; Artificial intelligence; Natural language processing; Human–computer interaction; Feature extraction; World Wide Web; Operating system","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":[0.00189483,0.001791729,0.00140464,0.006248645,0.0004448269,0.0007964536,0.0006730847,0.0005333272,0.007169981],"category_scores_gemma":[0.01950536,0.0004642115,0.0006250154,0.002476897,0.0002023649,0.001351284,0.0008679417,0.0007664498,0.003499466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003202735,"about_ca_system_score_gemma":0.000590608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002141928,"about_ca_topic_score_gemma":0.00298242,"domain_scores_codex":[0.9965322,0.0005300606,0.0008093817,0.0005022561,0.001413979,0.0002120571],"domain_scores_gemma":[0.9737386,0.01003763,0.002056881,0.001758683,0.0116865,0.0007217625],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00251983,0.0013556,0.1385466,0.003955178,0.0004090366,0.001251199,0.003157947,0.004420693,0.09464744,0.001272986,0.04707059,0.7013928],"study_design_scores_gemma":[0.000433452,0.005924413,0.7270737,0.0005073911,0.000418204,0.001843157,0.00157393,0.06328629,0.1476032,0.001981515,0.04868703,0.0006677689],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6955272,0.001070499,0.1731129,0.0003168145,0.0003267204,0.009289291,0.04481203,0.05791219,0.01763238],"genre_scores_gemma":[0.7311739,0.0007044503,0.2095131,0.0002500005,0.0001846462,0.01303973,0.02862276,0.004710128,0.01180119],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007169981,"threshold_uncertainty_score":0.02398604,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03685267458200683,"score_gpt":0.2483980203832389,"score_spread":0.2115453458012321,"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."}}