{"id":"W2577484822","doi":"10.1109/lra.2017.2651945","title":"Modeling Grasp Motor Imagery Through Deep Conditional Generative Models","year":2017,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"GRASP; Computer science; Artificial intelligence; Generative model; Process (computing); Generative grammar; Object (grammar); Task (project management); Action (physics); Human–computer interaction; Deep learning; Machine learning; Engineering; Systems engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00006835726,0.0001385874,0.0001340617,0.00005382816,0.0004407351,0.0002923716,0.00009454541,0.00005483785,0.00001524595],"category_scores_gemma":[0.00001201227,0.0001471734,0.00004408224,0.00002354385,0.00004402709,0.0008608869,0.00001495501,0.0001312014,0.00001784502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003421414,"about_ca_system_score_gemma":0.000005732277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001509591,"about_ca_topic_score_gemma":0.00000331653,"domain_scores_codex":[0.9993219,0.0000179447,0.000199016,0.000150477,0.0001474305,0.0001632205],"domain_scores_gemma":[0.9996625,0.00001675097,0.00005849649,0.0001790741,0.00003648652,0.00004674336],"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":[0.000001067198,0.000003807746,0.00002891515,0.00001869261,0.00002361733,0.000003222237,0.0002846697,0.9881779,0.008041389,0.002881918,0.000246386,0.0002884126],"study_design_scores_gemma":[0.0002204675,0.000004509101,0.0009421579,0.00001910776,0.00001273992,0.000004883405,0.00002221135,0.9964469,0.0002214785,0.001912485,0.00001599994,0.0001770676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08466089,0.00005213325,0.9131423,0.001113879,0.0003663656,0.00009248828,0.000002012443,0.0001979468,0.000372022],"genre_scores_gemma":[0.9707351,0.00003466956,0.02837878,0.0005506503,0.0002055285,0.00001018046,0.00003252697,0.00002642075,0.00002619433],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8860742,"threshold_uncertainty_score":0.6001557,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03599566429412324,"score_gpt":0.2506917033094166,"score_spread":0.2146960390152934,"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."}}