{"id":"W2111175667","doi":"10.1145/964696.964705","title":"<i>TiltText</i>","year":2003,"lang":"en","type":"article","venue":"","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":197,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"University of Toronto; Microsoft Research","keywords":"Text entry; Keypad; Computer science; Mobile phone; Ambiguity; Phone; Alphabet; Key (lock); Word error rate; Speech recognition; Character (mathematics); Natural language processing; Orientation (vector space); Information retrieval; Artificial intelligence; Human–computer interaction; Telecommunications; Linguistics; Mathematics; Computer security; Programming language","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":[0.0002320488,0.0004861157,0.0002591916,0.0003907193,0.0002976799,0.0007273344,0.0006741477,0.0004153314,0.03979952],"category_scores_gemma":[0.001748575,0.0002036602,0.0002050581,0.000448667,0.0004382509,0.001503121,0.001020984,0.0005167163,0.01277815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001503333,"about_ca_system_score_gemma":0.0001860793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006190396,"about_ca_topic_score_gemma":0.001190276,"domain_scores_codex":[0.9996979,0.00005056703,0.00002460753,0.00004702069,0.0001430283,0.00003692545],"domain_scores_gemma":[0.999271,0.0001779143,0.00008414927,0.0002141162,0.0001771597,0.00007571049],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007842585,0.0001193945,0.001609725,0.001143876,0.00002953019,0.000778453,0.0008531226,0.0005275088,0.3383563,0.01153033,0.1723047,0.4719628],"study_design_scores_gemma":[0.00006816669,0.0007948343,0.007580592,0.0001319338,0.00004780868,0.004137225,0.000311027,0.00602633,0.2381471,0.002370969,0.7402835,0.0001006245],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.08567674,0.00252555,0.7134493,0.001823387,0.00282437,0.0008967122,0.004386706,0.04453504,0.1438822],"genre_scores_gemma":[0.3500283,0.00246751,0.4642187,0.002077182,0.0007815125,0.001222452,0.009573141,0.005814543,0.1638167],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.03979952,"threshold_uncertainty_score":0.1331427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00801273467879511,"score_gpt":0.2251194638196488,"score_spread":0.2171067291408537,"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."}}