{"id":"W2099097872","doi":"10.1109/tepm.2008.926118","title":"Automatic Microassembly Using Visual Servo Control","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Electronics Packaging Manufacturing","topic":"Advanced MEMS and NEMS Technologies","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Micromanipulator; Cartesian coordinate system; GRASP; Servo; Microelectromechanical systems; Process (computing); Computer science; Position (finance); Servo control; Artificial intelligence; Computer vision; Position sensor; Control engineering; Engineering; Mechanical engineering","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":[0.0002202665,0.0004402718,0.0002879013,0.000342282,0.0002621598,0.0004645333,0.0008018723,0.0003855191,0.001239215],"category_scores_gemma":[0.0004218177,0.0002516489,0.0002605581,0.0002037447,0.0003660404,0.0004267439,0.0004689705,0.0003588791,0.0004074344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002426406,"about_ca_system_score_gemma":0.0003739072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009716552,"about_ca_topic_score_gemma":0.0008718328,"domain_scores_codex":[0.9994835,0.00003933274,0.00002369724,0.00009205266,0.0003259768,0.00003538324],"domain_scores_gemma":[0.9997433,0.00006745685,0.00005676191,0.00004102781,0.00007120823,0.00002035111],"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":[0.0001577289,0.00008505884,0.000389161,0.0001930608,0.00002170189,0.0001554136,0.0001426043,0.02013119,0.611702,0.006516537,0.001309326,0.3591962],"study_design_scores_gemma":[0.0001697294,0.0008334209,0.001853141,0.00003963875,0.00003827931,0.0006363703,0.0000344048,0.5576437,0.3992329,0.004021723,0.03538179,0.000115006],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03226146,0.0006446005,0.9590409,0.0001093474,0.0001237939,0.00008820734,0.00002239673,0.002922584,0.00478671],"genre_scores_gemma":[0.5578611,0.000410351,0.4357862,0.0001153715,0.00009172651,0.0001745101,0.00007960162,0.0001152555,0.0053658],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001239215,"threshold_uncertainty_score":0.004145563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0109565698510792,"score_gpt":0.2250604936179124,"score_spread":0.2141039237668332,"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."}}