{"id":"W17515232","doi":"","title":"Sign Language MMS to Make Cell Phones Accessible to the Deaf and Hard-of-hearing Community.","year":2007,"lang":"en","type":"article","venue":"Acta Orthopaedica Belgica","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Phone; Sign language; Videotelephony; Multimedia; Deaf community; Context (archaeology); Camera phone; Animation; Human–computer interaction; Artificial intelligence; Computer graphics (images)","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005618982,0.0006507462,0.0003928499,0.0008320279,0.0006826118,0.000927951,0.0006203505,0.0009510941,0.3388265],"category_scores_gemma":[0.003593714,0.0001196481,0.0002763536,0.0004729092,0.0004190133,0.001536989,0.001984415,0.0009117494,0.1683114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001973393,"about_ca_system_score_gemma":0.0006560314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008316574,"about_ca_topic_score_gemma":0.001950488,"domain_scores_codex":[0.9996138,0.00009530832,0.0000429101,0.00003260558,0.000143142,0.00007229712],"domain_scores_gemma":[0.9987901,0.0003309372,0.0001126763,0.0001909795,0.0003489435,0.0002264033],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00051097,0.0002800195,0.003145051,0.0005591561,0.00001404311,0.002051053,0.0006482675,0.00006147647,0.0199522,0.005122134,0.3380287,0.6296269],"study_design_scores_gemma":[0.0001002938,0.0005006985,0.006905997,0.0003113548,0.00001850188,0.007412226,0.00077953,0.0002957216,0.007043256,0.001716259,0.9748715,0.00004465866],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.07971024,0.004162108,0.03376318,0.01049372,0.009372231,0.001031609,0.009825742,0.01145554,0.8401855],"genre_scores_gemma":[0.1239658,0.001731993,0.01684446,0.005476889,0.0003826966,0.0005854134,0.002590597,0.0009405173,0.8474815],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3388265,"threshold_uncertainty_score":0.9430838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02626888021191892,"score_gpt":0.2764363396770873,"score_spread":0.2501674594651683,"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."}}