{"id":"W2042967086","doi":"10.5555/2447556.2447679","title":"Gestures for industry: intuitive human-robot communication from human observation","year":2013,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":82,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Gesture; Computer science; Human–robot interaction; Task (project management); Human–computer interaction; Context (archaeology); Robot; Set (abstract data type); Lexicon; Artificial intelligence; Engineering; Systems engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001837679,0.00115757,0.0006117993,0.001606171,0.0009955819,0.002245801,0.0008504595,0.001324156,0.005400998],"category_scores_gemma":[0.008976669,0.0006117445,0.0006495193,0.000804071,0.002524147,0.004913167,0.003093257,0.0009022124,0.001674602],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007606448,"about_ca_system_score_gemma":0.001199401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002765847,"about_ca_topic_score_gemma":0.002859831,"domain_scores_codex":[0.9977385,0.001147754,0.0001628282,0.0003882863,0.0004677565,0.00009480601],"domain_scores_gemma":[0.9970512,0.001658758,0.0003468037,0.0004912897,0.0003416784,0.0001101423],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001244455,0.0002126403,0.01518646,0.002828807,0.0001064618,0.001308106,0.01778126,0.02520385,0.1418519,0.117049,0.01055832,0.6666687],"study_design_scores_gemma":[0.0002345246,0.001132238,0.05829323,0.001406,0.0002047928,0.004623742,0.01619372,0.4570596,0.08198503,0.2552645,0.1229468,0.0006559724],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04560842,0.000518541,0.9398862,0.0004929031,0.00004598343,0.0005249587,0.0008589557,0.002245715,0.009818399],"genre_scores_gemma":[0.5638653,0.0004986107,0.4284551,0.0001534056,0.00004683325,0.001273466,0.001593086,0.0004094556,0.003704729],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005400998,"threshold_uncertainty_score":0.01806808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07553752340890948,"score_gpt":0.3144638627444046,"score_spread":0.2389263393354951,"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."}}