{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006496784,0.0001862731,0.0002527164,0.0001072671,0.0002742277,0.0006306263,0.0005587807,0.00005455726,0.00000659292],"category_scores_gemma":[0.0001115982,0.000161023,0.00003953473,0.0003849229,0.00007769364,0.001191363,0.0003176924,0.0002079466,0.000004036049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002490653,"about_ca_system_score_gemma":0.00002154572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002572136,"about_ca_topic_score_gemma":0.0004199239,"domain_scores_codex":[0.9983599,0.0002126351,0.0003625025,0.0005842459,0.0002343497,0.0002464009],"domain_scores_gemma":[0.998902,0.0002990812,0.0001952467,0.0004369012,0.00006222638,0.000104517],"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":[0.00000463287,0.00002527937,0.005814012,0.00003004504,0.00001030586,0.000003634491,0.005130086,0.00003163034,0.002998832,0.00006300565,0.00001280979,0.9858757],"study_design_scores_gemma":[0.001099151,0.00008567435,0.09419649,0.0000738539,0.00002450152,0.00006665998,0.0009192076,0.8902171,0.01223716,0.0001925623,0.0002737558,0.0006138473],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8294166,0.00420906,0.164714,0.0009569215,0.0001363951,0.0002002718,0.000003336302,0.0001742404,0.0001891646],"genre_scores_gemma":[0.9765891,0.00002076719,0.02247117,0.0004558696,0.000406441,0.000005686755,0.00001849985,0.000009120095,0.00002331759],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9852619,"threshold_uncertainty_score":0.6566326,"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."}}