{"id":"W2750460922","doi":"10.1109/hri.2016.7451898","title":"Optimal gaze-based robot selection in multi-human multi-robot interaction","year":2016,"lang":"en","type":"article","venue":"2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Gaze; Computer science; Human–robot interaction; Robot; Artificial intelligence; Selection (genetic algorithm); Computer vision; Human–computer interaction; Robot control; Mobile robot","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005857674,0.0007081825,0.0005943312,0.001685019,0.0004013318,0.0005181135,0.002433707,0.0004142648,0.001129943],"category_scores_gemma":[0.0004456954,0.0006046377,0.0002696054,0.0005281719,0.000210913,0.002045719,0.0003269597,0.001120083,0.001181232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001353793,"about_ca_system_score_gemma":0.0002306181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004840812,"about_ca_topic_score_gemma":0.001249032,"domain_scores_codex":[0.995333,0.0003497234,0.001205901,0.001561665,0.0007604729,0.0007891943],"domain_scores_gemma":[0.9964936,0.0004008609,0.000894397,0.0009151716,0.001082874,0.0002130937],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005417474,0.005808384,0.008114212,0.0000633594,0.0003789402,0.0001374901,0.0008514384,0.01730064,0.8616153,0.04035507,0.00588055,0.05895289],"study_design_scores_gemma":[0.01934832,0.002997164,0.1199731,0.00588844,0.00009965204,0.0003612931,0.0007674441,0.506089,0.3258975,0.002503163,0.01178587,0.004289049],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1074865,0.00001606458,0.8782435,0.006569101,0.004442894,0.0006023452,0.00002821362,0.001030742,0.001580629],"genre_scores_gemma":[0.9718028,0.00003009532,0.02326608,0.0005032765,0.0003012709,0.000201168,0.00004816149,0.00006250486,0.003784638],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8643163,"threshold_uncertainty_score":0.9997832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.250824737054364,"score_gpt":0.428233806082312,"score_spread":0.177409069027948,"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."}}