{"id":"W3158050854","doi":"10.1016/j.patrec.2021.04.017","title":"Object recognition in performed basic daily activities with a handcrafted data glove prototype","year":2021,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Object (grammar); Computer science; Computer vision; Artificial intelligence; Wired glove; Computer graphics (images); Engineering drawing; Engineering; Virtual reality","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003162204,0.0004239423,0.0004654641,0.0002465702,0.0001122989,0.0003920153,0.0008675316,0.0007001258,0.007569077],"category_scores_gemma":[0.0006069006,0.0002344284,0.0002901052,0.000150276,0.0001587562,0.0004272932,0.0005187515,0.0002793164,0.002268172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001548065,"about_ca_system_score_gemma":0.0002883578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001583845,"about_ca_topic_score_gemma":0.002069356,"domain_scores_codex":[0.9997873,0.00002303938,0.00001638114,0.00006568204,0.00008044892,0.0000272625],"domain_scores_gemma":[0.9997782,0.00005664946,0.00001077166,0.00004295827,0.00008348624,0.00002798885],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001809,0.0003530872,0.005266643,0.0003003826,0.00009576413,0.0005442058,0.0002494273,0.005483331,0.5500201,0.0008554784,0.003942191,0.4310804],"study_design_scores_gemma":[0.0003347228,0.002989248,0.05017824,0.000121381,0.0001850568,0.004578942,0.0002401429,0.3736062,0.5474393,0.001028307,0.01913699,0.0001615306],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3907078,0.0004290806,0.5915102,0.0002073955,0.0002290703,0.0004545413,0.0009088368,0.0101949,0.005358158],"genre_scores_gemma":[0.8273962,0.0001818362,0.1552565,0.0002368657,0.00001855304,0.0002916804,0.0008419013,0.0002934737,0.01548296],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007569077,"threshold_uncertainty_score":0.02532107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04651275214107464,"score_gpt":0.2504616354469917,"score_spread":0.203948883305917,"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."}}