{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002797967,0.0004666225,0.0003467106,0.0003207487,0.0002348963,0.0002661823,0.0006890867,0.0005252886,0.0009865983],"category_scores_gemma":[0.0006095765,0.000365754,0.0003643182,0.0002203627,0.0003419823,0.0005320685,0.0004528204,0.0003230941,0.0002649922],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002119959,"about_ca_system_score_gemma":0.0002272435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000544343,"about_ca_topic_score_gemma":0.0007664677,"domain_scores_codex":[0.9998196,0.00002535939,0.00001383256,0.00004079721,0.00008503592,0.00001546729],"domain_scores_gemma":[0.9996917,0.0001118133,0.00005533171,0.0000840517,0.00004102061,0.00001614265],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001986053,0.00005669272,0.001228669,0.0002131044,0.00005306974,0.0005485676,0.0002162657,0.09769522,0.5370059,0.004281388,0.001323137,0.3571795],"study_design_scores_gemma":[0.00007817336,0.0007068786,0.006413285,0.00004789569,0.00006443717,0.003814912,0.00006513049,0.7522171,0.2173021,0.003839438,0.01530044,0.0001502497],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05581615,0.0002027247,0.942072,0.00003898916,0.00001948887,0.00003020885,0.00002098285,0.001029389,0.0007699915],"genre_scores_gemma":[0.3546396,0.0001396068,0.6443179,0.0000408022,0.000007433411,0.00005922344,0.00004463625,0.00005920356,0.0006916092],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0009865983,"threshold_uncertainty_score":0.003300548,"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."}}