{"id":"W2166039043","doi":"10.1044/aac17.4.156","title":"Words We Would Want: Comparison of Three Pre-programmed Vocabulary Sets With Frequently Used Words in English","year":2008,"lang":"en","type":"article","venue":"Perspectives on Augmentative and Alternative Communication","topic":"Text Readability and Simplification","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Glenrose Rehabilitation Hospital","funders":"","keywords":"Vocabulary; Computer science; Word (group theory); Gateway (web page); Set (abstract data type); English vocabulary; Natural language processing; Word lists by frequency; Artificial intelligence; Speech recognition; Linguistics; World Wide Web","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":[0.0003662478,0.000249244,0.000383767,0.0002579382,0.0002360459,0.00006593725,0.0008741466,0.00008928329,0.000009698102],"category_scores_gemma":[0.00009288542,0.0002159196,0.00005560182,0.0005980959,0.0006401993,0.0007730542,0.0002168868,0.0005637351,0.000002471883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000249466,"about_ca_system_score_gemma":0.00006561144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004577087,"about_ca_topic_score_gemma":0.00062644,"domain_scores_codex":[0.9978784,0.0004667117,0.0004268879,0.000556567,0.0004325227,0.0002388646],"domain_scores_gemma":[0.9978381,0.0004726526,0.0003984727,0.0009054469,0.000303472,0.00008189405],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0003767794,0.001903915,0.148238,0.00003356945,0.0001577901,0.000005453758,0.7502274,0.000400524,0.0007259125,0.05038866,0.00002244689,0.04751953],"study_design_scores_gemma":[0.00547097,0.002586179,0.7607512,0.0006076294,0.00004202202,0.00001305464,0.1320466,0.04928231,0.02610544,0.02185776,0.0002860131,0.0009508114],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.925882,0.001611998,0.06920666,0.001502416,0.00004066629,0.0007650736,0.00001491418,0.0000887757,0.0008874876],"genre_scores_gemma":[0.9853525,0.001065599,0.01333006,0.00004131501,0.00001475305,0.0001335836,0.00002670952,0.00001370973,0.00002179447],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6181809,"threshold_uncertainty_score":0.8804947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06367648814629527,"score_gpt":0.3369147263330553,"score_spread":0.27323823818676,"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."}}