{"id":"W1520872323","doi":"10.1109/icnn.1995.487810","title":"A connectionist model of human grasps and its application to robot grasping","year":2002,"lang":"en","type":"article","venue":"","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"GRASP; Connectionism; Computer science; Artificial intelligence; Object (grammar); Task (project management); Domain (mathematical analysis); Grippers; Robot; Artificial neural network; Engineering; Mathematics","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":[0.0002642741,0.000645282,0.0003869238,0.0004551412,0.0004069405,0.0008393892,0.001033706,0.001530163,0.003086753],"category_scores_gemma":[0.001162924,0.0004408258,0.0006543669,0.0005934626,0.0007405698,0.001689902,0.0004545375,0.001354071,0.0006949333],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009046545,"about_ca_system_score_gemma":0.0005476515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006359561,"about_ca_topic_score_gemma":0.004846295,"domain_scores_codex":[0.9999001,0.00001665064,0.000005938281,0.00002742463,0.00003393958,0.00001583315],"domain_scores_gemma":[0.999816,0.00009830383,0.00001787758,0.00002349137,0.00002748862,0.00001679821],"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":[0.0000943694,0.00008257353,0.0008634387,0.000134641,0.0001235231,0.0003796405,0.0001399155,0.750046,0.005789574,0.1566982,0.004057245,0.0815909],"study_design_scores_gemma":[0.00001026593,0.00004179496,0.0003028165,0.00001167588,0.00001600691,0.0001271757,0.000005052969,0.9165227,0.0007301168,0.07933591,0.002879336,0.00001720906],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03137695,0.001590806,0.9437337,0.002261393,0.0002732672,0.00005067301,0.0001907052,0.0008544888,0.01966809],"genre_scores_gemma":[0.8539183,0.00372246,0.121335,0.0003648705,0.000408449,0.000177661,0.0003285713,0.000155074,0.0195897],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006359561,"threshold_uncertainty_score":0.01264507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04535190857217485,"score_gpt":0.2443056617704476,"score_spread":0.1989537531982727,"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."}}