{"id":"W2518255983","doi":"","title":"Towards a robust framework for visual human-robot interaction","year":2012,"lang":"en","type":"article","venue":"","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Robot; Visual servoing; Artificial intelligence; Computer science; Mobile robot; Human–robot interaction; Interface (matter); Human–computer interaction; Computer vision; Robot learning; Personal robot; Social robot; Robot control; Set (abstract data type)","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":[],"consensus_categories":[],"category_scores_codex":[0.0002403904,0.00009515884,0.00008618328,0.00002508592,0.0001112158,0.00003135098,0.0001890692,0.0001162224,0.0007115303],"category_scores_gemma":[0.0001215376,0.00007946718,0.00004527834,0.00008859164,0.00008069949,0.0003703131,0.0002368125,0.0001258889,0.0004335782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001766317,"about_ca_system_score_gemma":0.000001324118,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002679721,"about_ca_topic_score_gemma":0.000009650761,"domain_scores_codex":[0.9992403,0.00001761781,0.000133607,0.000158115,0.0001437981,0.0003065885],"domain_scores_gemma":[0.9996538,0.00005602453,0.0000430346,0.0001926597,0.000003786039,0.00005070261],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001217131,0.001841186,0.5505031,0.00009928925,0.0001097463,0.000002359171,0.00501146,0.002629571,0.146357,0.1154318,0.03698962,0.1409031],"study_design_scores_gemma":[0.0004302113,0.0004530813,0.2434112,0.00005458015,0.00003897372,0.00001030388,0.002214812,0.0006293594,0.6463473,0.06326529,0.04233067,0.00081419],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7763264,0.000007495402,0.2183276,0.0006776164,0.0006953658,0.0002142078,0.000001157766,0.0004793937,0.003270766],"genre_scores_gemma":[0.8510477,0.000001005101,0.1481412,0.00006167491,0.0001762943,0.00005107747,0.000002202113,0.00001064452,0.0005082042],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4999904,"threshold_uncertainty_score":0.7790757,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.110288412052464,"score_gpt":0.3746519589519395,"score_spread":0.2643635468994755,"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."}}