{"id":"W2159203809","doi":"10.1109/tic-sth.2009.5444533","title":"Analysis of text entry performance metrics","year":2009,"lang":"en","type":"article","venue":"","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":197,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Text entry; Computer science; Data entry; Task (project management); Mobile device; Word error rate; Human–computer interaction; Information retrieval; Artificial intelligence; World Wide Web; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006844653,0.000049261,0.0001188384,0.0004239778,0.00002504393,0.00001818894,0.0003501358,0.00001713018,0.0001025894],"category_scores_gemma":[0.00001776301,0.00003939603,0.0001001547,0.002064253,0.00000813867,0.0003667813,0.00003535118,0.00003991659,0.00003885062],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001383533,"about_ca_system_score_gemma":0.00001144735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000816518,"about_ca_topic_score_gemma":6.752376e-7,"domain_scores_codex":[0.9995067,0.00001068907,0.0001120762,0.0001277897,0.0001365659,0.0001062369],"domain_scores_gemma":[0.9995403,0.00003679999,0.00005159379,0.0002359002,0.000110091,0.00002532887],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00006434622,0.001095823,0.1893279,0.00002394382,0.002419064,0.00001667939,0.003366509,0.002543899,0.1187264,0.3401096,0.01448982,0.327816],"study_design_scores_gemma":[0.0001076031,0.0001701007,0.6601546,0.00000356987,0.000107916,9.932187e-7,0.00005397053,0.2232182,0.1151194,0.00005669884,0.0009004988,0.0001064119],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3775968,0.00005594306,0.4683069,0.0002867717,0.00009847209,0.00003735568,0.000001143047,0.00001165493,0.1536049],"genre_scores_gemma":[0.9962947,0.00002260982,0.002068734,0.0007034575,0.000006202241,3.075521e-7,0.000001591353,6.986524e-7,0.0009016756],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6186979,"threshold_uncertainty_score":0.1606523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01104203886188412,"score_gpt":0.2544068540875464,"score_spread":0.2433648152256623,"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."}}