{"id":"W2482261486","doi":"10.1109/iecon.1992.254481","title":"Sensing and control for automated robotic edge deburring","year":2003,"lang":"en","type":"article","venue":"","topic":"Iterative Learning Control Systems","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Artificial intelligence; Enhanced Data Rates for GSM Evolution; Computer science; Computer vision; Task (project management); Planner; Chamfer (geometry); Robot end effector; Control engineering; Engineering; Robot","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":[0.0002734024,0.0003689783,0.0002560347,0.0001808009,0.0003245246,0.00062615,0.0006063617,0.0005282736,0.002392013],"category_scores_gemma":[0.000629281,0.0002044868,0.0002348698,0.0001676543,0.0004953978,0.0004941266,0.000519364,0.000607191,0.0005038365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004133608,"about_ca_system_score_gemma":0.0004725203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0027497,"about_ca_topic_score_gemma":0.002739555,"domain_scores_codex":[0.9996617,0.00003916194,0.00001585969,0.00007154936,0.0001860231,0.00002578751],"domain_scores_gemma":[0.9997823,0.0000762462,0.00003939646,0.00003219434,0.00005945007,0.00001037457],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001678521,0.0001117139,0.0006062054,0.0002161814,0.0000342229,0.0001942877,0.0003671642,0.2920475,0.1411133,0.04171943,0.005101039,0.518321],"study_design_scores_gemma":[0.00004905947,0.0002689031,0.0007339244,0.00002973922,0.00001989331,0.0001648854,0.00004023333,0.9147494,0.03965575,0.01452892,0.02971543,0.0000438627],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01040522,0.0003869066,0.9808221,0.000157611,0.00004763671,0.00005141343,0.00001980025,0.0009608449,0.007148437],"genre_scores_gemma":[0.703037,0.0005522404,0.2821122,0.0002668237,0.00008429373,0.0002068683,0.00008821725,0.0000937584,0.01355857],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0027497,"threshold_uncertainty_score":0.008002102,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006958405550242453,"score_gpt":0.2114448422089711,"score_spread":0.2044864366587287,"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."}}