{"id":"W2778092082","doi":"10.1109/tii.2017.2785415","title":"Mind Control of a Robotic Arm With Visual Fusion Technology","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":149,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Fundamental Research Funds for the Central Universities; State Key Laboratory of Robotics and System; Natural Science Foundation of Guangdong Province","keywords":"Computer vision; Artificial intelligence; Computer science; Robotic arm; Object (grammar); Visual servoing; Task (project management); Motion control; Obstacle avoidance; Robot; Control system; Mobile robot; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000127051,0.0001685831,0.0002829609,0.0002815138,0.0004204448,0.000121569,0.000477981,0.0002431028,0.00005725509],"category_scores_gemma":[0.00005852877,0.0001291728,0.00006520715,0.000177361,0.0003758073,0.0004342216,0.000004079829,0.0005044545,0.00004079832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003014183,"about_ca_system_score_gemma":0.00009221874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001340886,"about_ca_topic_score_gemma":0.000008972761,"domain_scores_codex":[0.9988472,0.00003042633,0.0004767028,0.0001285507,0.0002772759,0.0002397772],"domain_scores_gemma":[0.9988185,0.0001853105,0.0004355223,0.0004321669,0.00006090383,0.00006760048],"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":[0.003299983,0.003040803,0.0004237597,0.0001802544,0.0002876235,0.00005235574,0.00515246,0.2659651,0.1331234,0.0009488747,0.0006478326,0.5868776],"study_design_scores_gemma":[0.003509472,0.001825578,0.000009504016,0.0001797881,0.0000579376,0.00005555839,0.0003541541,0.04763189,0.9455447,0.00004368438,0.0005832528,0.0002044598],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6423175,0.000001162138,0.3549378,0.0004610376,0.0008919099,0.0003735022,0.00003134781,0.00005128739,0.0009344685],"genre_scores_gemma":[0.9991178,0.000004491591,0.0005265411,0.0001123539,0.00005264871,0.00001406974,3.200698e-7,0.00001030138,0.0001614902],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8124214,"threshold_uncertainty_score":0.5267514,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05104758141838629,"score_gpt":0.2921254179642555,"score_spread":0.2410778365458692,"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."}}