{"id":"W4390772744","doi":"10.3390/machines12010045","title":"Action Recognition for Human–Robot Teaming: Exploring Mutual Performance Monitoring Possibilities","year":2024,"lang":"en","type":"article","venue":"Machines","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Commission; Canadian Institute of Steel Construction","keywords":"Anticipation (artificial intelligence); Human–robot interaction; Human–computer interaction; Computer science; Action (physics); Robot; Knowledge management; Artificial intelligence; Function (biology)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002265224,0.0001391948,0.0001191082,0.0002129742,0.0002909117,0.0001305342,0.00007528747,0.00006447145,0.001509345],"category_scores_gemma":[0.00003574035,0.0001310497,0.0000950195,0.0001051577,0.00002508829,0.0005430053,0.00001580884,0.0001859969,0.000584779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006708089,"about_ca_system_score_gemma":0.00001014355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006936131,"about_ca_topic_score_gemma":0.00001429307,"domain_scores_codex":[0.9990765,0.00004663637,0.0002887354,0.0002833169,0.0001086129,0.000196218],"domain_scores_gemma":[0.9995326,0.0001651014,0.0000501436,0.0001519278,0.00006114171,0.00003905917],"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":[0.0002170618,0.0001419207,0.01330215,0.0006418875,0.0001886347,0.000006985536,0.02480229,0.00008078779,0.00979729,0.004414983,0.003348425,0.9430576],"study_design_scores_gemma":[0.004249955,0.001750779,0.6484261,0.002472533,0.000354905,0.0003498619,0.03462359,0.0414638,0.0495481,0.02150063,0.1924354,0.00282435],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9775775,0.0001375822,0.001782413,0.0001350461,0.008275088,0.0002109384,0.0000174159,0.0006196194,0.01124437],"genre_scores_gemma":[0.9822753,0.00001800975,0.0002979785,0.00002514368,0.001722629,0.0004436429,0.00005478458,0.00003303524,0.01512949],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9402332,"threshold_uncertainty_score":0.9994034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2775772052642138,"score_gpt":0.4477326932679347,"score_spread":0.1701554880037209,"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."}}