{"id":"W4402980576","doi":"10.1109/otcon60325.2024.10687996","title":"Human-Machine Interaction Systems for Training Industrial Robots","year":2024,"lang":"en","type":"article","venue":"","topic":"Ergonomics and Human Factors","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Computer science; Robot; Training (meteorology); Human–robot interaction; Human–computer interaction; Artificial intelligence","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":[0.001121368,0.0005649539,0.0003085115,0.0003782425,0.0004328848,0.0007392886,0.0007738033,0.0007069986,0.02431673],"category_scores_gemma":[0.002450072,0.0001894739,0.0002520562,0.0002778476,0.0002684319,0.0007972699,0.0008465265,0.0004939552,0.005057229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005028411,"about_ca_system_score_gemma":0.0005362143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001629337,"about_ca_topic_score_gemma":0.002303545,"domain_scores_codex":[0.9995016,0.0001883122,0.00003131602,0.00007864398,0.0001615767,0.00003850791],"domain_scores_gemma":[0.9993012,0.0003422688,0.00005148221,0.00007739199,0.0001792708,0.00004836923],"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":[0.0003616613,0.000449772,0.003015957,0.0009013914,0.0000617301,0.0001558949,0.0007768321,0.01729809,0.03968908,0.006701397,0.02318234,0.9074059],"study_design_scores_gemma":[0.0003076502,0.004773357,0.04419068,0.001159786,0.0002047896,0.001303113,0.001896838,0.4903283,0.05407607,0.03031591,0.3711556,0.0002878328],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05820026,0.00432866,0.8793612,0.001634905,0.0005786402,0.00113414,0.0005891987,0.01082775,0.04334522],"genre_scores_gemma":[0.5837079,0.001690132,0.3842545,0.0006649152,0.0001377151,0.001567628,0.0009152163,0.0002886901,0.0267732],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02431673,"threshold_uncertainty_score":0.08134758,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1002115498740189,"score_gpt":0.2853025397132453,"score_spread":0.1850909898392264,"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."}}