{"id":"W4386838331","doi":"10.18280/ria.370420","title":"Non-Invasive Tongue-Based HCI System Using Deep Learning for Microgesture Detection","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Voice and Speech Disorders","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Tongue; Deep learning; Artificial intelligence; Human–computer interaction; Medicine; Pathology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003487778,0.0001910802,0.0003051454,0.0002524096,0.0003330161,0.00004108086,0.0001120721,0.0001646977,0.00003425623],"category_scores_gemma":[0.0002812292,0.0001896619,0.00022816,0.0007795275,0.00005353264,0.00007073684,0.00002653333,0.0002484749,0.000426792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001478137,"about_ca_system_score_gemma":0.00009307874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004752928,"about_ca_topic_score_gemma":0.00005866344,"domain_scores_codex":[0.9985887,0.00003014936,0.0003717272,0.0004085424,0.0001631273,0.000437757],"domain_scores_gemma":[0.9990251,0.0002138523,0.0001226496,0.0003054751,0.0002152268,0.0001176887],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002845091,0.0001341038,0.006133374,0.002344018,0.00009616027,0.0001162391,0.001973324,0.2906489,0.6654889,0.00008523672,0.0002217477,0.03247344],"study_design_scores_gemma":[0.0001428181,0.0002177451,0.0001252748,0.0003753504,0.00007973002,0.0000395765,0.00894356,0.5663098,0.4219351,0.00001615089,0.001664212,0.0001506413],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7840798,0.0002399457,0.2127687,0.0003982682,0.0004356907,0.00101154,0.000003450495,0.0003511135,0.0007113827],"genre_scores_gemma":[0.9975334,0.00004125414,0.000762366,0.0001278285,0.0002113744,0.00007846006,0.00004463949,0.00005064008,0.00115],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2756608,"threshold_uncertainty_score":0.7734189,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0414229182986799,"score_gpt":0.2964167679745943,"score_spread":0.2549938496759144,"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."}}