{"id":"W2169244603","doi":"10.1109/3477.931540","title":"Classification of grasps by robot hands","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"GRASP; Linear subspace; Object (grammar); Kinematics; Artificial intelligence; Interpretation (philosophy); Computer science; Robot; Contact force; Computer vision; Space (punctuation); Mathematics; Geometry; Physics","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"],"consensus_categories":[],"category_scores_codex":[0.0001739768,0.0002969849,0.0003532133,0.0001914459,0.0001258195,0.00009640735,0.0001493859,0.0002126725,0.0001259796],"category_scores_gemma":[0.000003426696,0.0003183815,0.00009403852,0.0002830332,0.000105654,0.00009552597,0.00000170434,0.0003151641,0.00009421953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005755475,"about_ca_system_score_gemma":0.00001165951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008433342,"about_ca_topic_score_gemma":0.00005735229,"domain_scores_codex":[0.998328,0.00008414706,0.0006111631,0.0003173909,0.0003335392,0.0003257647],"domain_scores_gemma":[0.9991601,0.00008141526,0.0001196933,0.0003722324,0.0000820757,0.0001844565],"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":[0.00004734752,0.0002152708,0.001471448,0.0002317591,0.0001855274,0.000005931467,0.0008628633,0.9643865,0.01270529,0.00121987,0.006631072,0.0120371],"study_design_scores_gemma":[0.002481561,0.0004945958,0.009457123,0.0004304867,0.0002403501,0.0001008356,0.0007701044,0.8784905,0.009616726,0.00008148926,0.09666866,0.001167542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.309233,0.001568185,0.6665657,0.0001443673,0.001740693,0.0007018097,0.00002139192,0.0005587529,0.01946608],"genre_scores_gemma":[0.9915503,0.001142554,0.00008576119,0.00002393567,0.00008720779,0.00004823676,0.00001864421,0.00007141548,0.006971942],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6823173,"threshold_uncertainty_score":0.9999268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02564064102734379,"score_gpt":0.2300902844383014,"score_spread":0.2044496434109576,"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."}}