{"id":"W2105331304","doi":"10.1145/1055674.1055676","title":"American Sign Language natural language generation and machine translation","year":2005,"lang":"en","type":"article","venue":"ACM SIGACCESS Accessibility and Computing","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Closed captioning; American Sign Language; Interpreter; Computer science; Reading (process); Machine translation; Sign language; Linguistics; American English; Literacy; Sign (mathematics); Natural language processing; Artificial intelligence; Psychology; Pedagogy; Programming language","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002366965,0.001030572,0.001058212,0.003014978,0.001263589,0.002302969,0.001335367,0.001439401,0.01847171],"category_scores_gemma":[0.005933304,0.0004580061,0.001059211,0.002337647,0.0009431249,0.00278667,0.002547091,0.0009974826,0.0133106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000731686,"about_ca_system_score_gemma":0.001877114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003802783,"about_ca_topic_score_gemma":0.004021112,"domain_scores_codex":[0.9977067,0.0008537535,0.0002436662,0.0005359764,0.0004736494,0.0001862522],"domain_scores_gemma":[0.9981244,0.0004635169,0.00011545,0.0005501266,0.0006783534,0.00006821302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000185608,0.0001240296,0.0009204911,0.0003965781,0.00006971631,0.0004321459,0.0002128816,0.006876139,0.01114995,0.03163763,0.05533591,0.8926588],"study_design_scores_gemma":[0.0001524554,0.0003201079,0.003263308,0.0002615993,0.0001524734,0.00206385,0.0005530815,0.4785406,0.04798583,0.2003162,0.2662149,0.0001755619],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007322453,0.002729141,0.9508559,0.001184637,0.001069407,0.0003729724,0.001386886,0.01629421,0.01878448],"genre_scores_gemma":[0.1555551,0.003684078,0.7932476,0.0007817299,0.0005409888,0.0007157818,0.008110899,0.001297691,0.03606617],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01847171,"threshold_uncertainty_score":0.06179404,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03063164577929895,"score_gpt":0.3076670828060249,"score_spread":0.277035437026726,"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."}}