{"id":"W1964478843","doi":"10.1145/634067.634256","title":"Measuring errors in text entry tasks","year":2001,"lang":"en","type":"article","venue":"","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":237,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Levenshtein distance; Computer science; Synchronization (alternating current); Statistic; String (physics); Measure (data warehouse); Overhead (engineering); Natural language processing; Word error rate; Artificial intelligence; Data mining; Statistics; Mathematics; Programming language; Telecommunications","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":[0.007282487,0.0009439848,0.001145014,0.003485482,0.0005253021,0.001998144,0.001304822,0.001463289,0.001431142],"category_scores_gemma":[0.1373956,0.000469609,0.0003843388,0.002539345,0.0007460741,0.004284557,0.001643406,0.001106793,0.001170218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004806116,"about_ca_system_score_gemma":0.00071039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000968894,"about_ca_topic_score_gemma":0.00101103,"domain_scores_codex":[0.9818212,0.005628789,0.002984495,0.001716606,0.007456963,0.0003918967],"domain_scores_gemma":[0.7988109,0.1435786,0.02542466,0.00850019,0.02198027,0.001705277],"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":[0.003957349,0.0006929073,0.1525958,0.001438688,0.0003819801,0.0002473204,0.00416217,0.01723207,0.08680235,0.003579135,0.002468669,0.7264416],"study_design_scores_gemma":[0.0003598306,0.007114886,0.531242,0.0003297425,0.000361724,0.002574108,0.003541184,0.2510438,0.180281,0.01625556,0.006184657,0.0007115038],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5919794,0.0007442846,0.3996986,0.0001625479,0.0001175594,0.0003918164,0.0008982711,0.002308072,0.003699475],"genre_scores_gemma":[0.8396655,0.0003310373,0.1552165,0.00007654228,0.00009444357,0.0004473057,0.001504563,0.000532417,0.002131599],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007282487,"threshold_uncertainty_score":0.0385139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03213864813664468,"score_gpt":0.2585450126643108,"score_spread":0.2264063645276662,"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."}}