{"id":"W2124414803","doi":"10.1109/robot.1997.620067","title":"3-D flexible fixturing using a multi-degree of freedom gripper for robotic fixtureless assembly","year":2002,"lang":"en","type":"article","venue":"","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Fixture; Fender; Sheet metal; GRASP; Automotive industry; Engineering; Point (geometry); Robot; Enhanced Data Rates for GSM Evolution; Grippers; Set (abstract data type); Mechanical engineering; Computer science; Engineering drawing; Artificial intelligence; Geometry; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008443993,0.0001536825,0.0002259664,0.0001278533,0.00007085112,0.00003240618,0.0001176415,0.00009862279,0.0003314398],"category_scores_gemma":[0.00003844281,0.0001498163,0.0001039801,0.0001571004,0.00001563262,0.0001637175,0.00002030356,0.000130287,0.00002396638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004151619,"about_ca_system_score_gemma":0.000003795782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003285741,"about_ca_topic_score_gemma":0.00002274826,"domain_scores_codex":[0.9991773,0.00001405675,0.0002752752,0.0001510587,0.0001183066,0.0002639672],"domain_scores_gemma":[0.9995937,0.00006893218,0.00004452581,0.0001823064,0.00005053769,0.00005994553],"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":[0.000002036202,0.00002058488,0.0004740497,0.0001112656,0.00002897481,7.866632e-7,0.000187819,0.9797127,0.01768096,0.0003020171,0.0004717416,0.00100712],"study_design_scores_gemma":[0.0005649374,0.00001602738,0.002187556,0.00005127911,0.00002014318,0.000004663357,0.0000571019,0.9929199,0.003605559,0.000007073701,0.0003773618,0.0001883897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02722225,0.0003589215,0.9673154,0.00003004726,0.0003466697,0.0002291629,4.881462e-7,0.0003450029,0.004152079],"genre_scores_gemma":[0.922581,0.00001027804,0.07561356,0.0000192756,0.0001012211,0.000008725196,0.000002864122,0.000048841,0.001614241],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8953587,"threshold_uncertainty_score":0.6109332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1289464217490061,"score_gpt":0.2771104580286565,"score_spread":0.1481640362796504,"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."}}