{"id":"W4319046153","doi":"","title":"Gestural Control at IRCAM","year":2001,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Control (management); Computer science; Communication; Artificial intelligence; Psychology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002751642,0.0003734626,0.0003907427,0.0002323641,0.0005379925,0.0007765985,0.002084284,0.0003252059,0.000377496],"category_scores_gemma":[0.0004389629,0.0003942004,0.0002909992,0.0003264561,0.0001443213,0.0003450725,0.001872711,0.0006987006,0.0004820716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002293141,"about_ca_system_score_gemma":0.0001903992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000389078,"about_ca_topic_score_gemma":0.0007327245,"domain_scores_codex":[0.9942113,0.003313352,0.0005299347,0.0009858973,0.0005147181,0.0004448506],"domain_scores_gemma":[0.9943457,0.0008837152,0.0005345582,0.002387854,0.001610191,0.0002379356],"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":[0.00005844338,0.002551355,0.007179257,0.0004867689,0.000616346,0.0001534398,0.02197393,0.0005890045,0.01022239,0.3087357,0.03878014,0.6086532],"study_design_scores_gemma":[0.007569295,0.000003937963,0.04229683,0.006077872,0.0003354881,0.0004729973,0.0001733194,0.3550369,0.1472299,0.09301179,0.3426991,0.005092598],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08527064,0.0007503749,0.7828504,0.01841938,0.0007913603,0.0006125015,0.00004826918,0.0008565921,0.1104005],"genre_scores_gemma":[0.9435564,0.0005168577,0.02879657,0.0006324637,0.00008699317,0.0001129923,0.0003237191,0.00004144013,0.02593258],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8582857,"threshold_uncertainty_score":0.999851,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01410915115563141,"score_gpt":0.2210468337837788,"score_spread":0.2069376826281474,"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."}}