{"id":"W4323668334","doi":"10.21203/rs.3.rs-2656781/v1","title":"Personalization of Industrial Human-Robot Communication through Domain Adaptation based on User Feedback","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Social Robot Interaction and HRI","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of Victoria; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Personalization; Computer science; Robot; Human–computer interaction; Task (project management); Human–robot interaction; Adaptation (eye); Domain (mathematical analysis); Artificial intelligence; Perception; Machine learning; 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":[0.001839457,0.0006902948,0.0005567378,0.0003827523,0.0002573713,0.0006105726,0.0007883601,0.0006676409,0.001687283],"category_scores_gemma":[0.00541081,0.0002553896,0.0004815081,0.0002660953,0.0004826827,0.0008696335,0.001544404,0.0008548541,0.0006115714],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003514068,"about_ca_system_score_gemma":0.000405107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001442313,"about_ca_topic_score_gemma":0.001507477,"domain_scores_codex":[0.9986616,0.0006251851,0.00004267686,0.0003592492,0.0002139802,0.00009736601],"domain_scores_gemma":[0.9978541,0.001087175,0.0001766563,0.000484834,0.0002742632,0.0001230107],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008196948,0.0008274019,0.01179535,0.0002393583,0.0001763953,0.0003275871,0.0009618069,0.4597886,0.06555949,0.003138333,0.003622825,0.4527432],"study_design_scores_gemma":[0.00001076958,0.0001822845,0.002619722,0.00001369144,0.00001508918,0.00007662129,0.00009439377,0.9825319,0.01094066,0.002098161,0.001393551,0.00002308963],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1639924,0.0002980238,0.8299132,0.0002390153,0.00006186795,0.0001162653,0.0001196788,0.002548208,0.002711331],"genre_scores_gemma":[0.941869,0.00008696867,0.05602626,0.00008026981,0.00002711935,0.00008764024,0.000143675,0.00007465176,0.0016044],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001839457,"threshold_uncertainty_score":0.009728134,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5138975442057956,"score_gpt":0.5426285622125774,"score_spread":0.02873101800678179,"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."}}