{"id":"W2914856714","doi":"10.1109/lra.2019.2896444","title":"Contactless Robotic Micromanipulation in Air Using a Magneto-Acoustic System","year":2019,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Acoustic levitation; Teleoperation; Workspace; Millimeter; Computer science; Process (computing); Orientation (vector space); Automation; Scalability; Levitation; Acoustics; Mechanical engineering; Magnet; Engineering; Artificial intelligence; Robot; Physics; Optics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002915298,0.0003079855,0.0003496784,0.0001591726,0.0003519194,0.0004603377,0.0006574183,0.000464729,0.00126231],"category_scores_gemma":[0.0003409708,0.0002876458,0.000197549,0.0001256382,0.0003978653,0.0004811073,0.0005324381,0.0003022867,0.0005795822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002868905,"about_ca_system_score_gemma":0.000477579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005916706,"about_ca_topic_score_gemma":0.00100136,"domain_scores_codex":[0.9995344,0.00003628894,0.0000232705,0.000119534,0.0002617907,0.00002478214],"domain_scores_gemma":[0.9998077,0.00007078866,0.00004598963,0.00003190862,0.00002729888,0.00001639345],"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":[0.00005781025,0.00002716306,0.0001571655,0.000135454,0.000007435937,0.00009421261,0.00007260807,0.001719178,0.9750705,0.001533477,0.000370923,0.0207541],"study_design_scores_gemma":[0.00008810443,0.0008357944,0.0021113,0.00003068308,0.00003285545,0.0007491144,0.0000447094,0.0850319,0.8660759,0.0007534919,0.04416538,0.00008091551],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2052232,0.002350959,0.7773967,0.0005234958,0.0004545728,0.0003299524,0.0002296574,0.003180802,0.01031074],"genre_scores_gemma":[0.6125844,0.000762852,0.3784227,0.0001434004,0.00008650141,0.0002516052,0.0001007226,0.00004916726,0.007598522],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00126231,"threshold_uncertainty_score":0.00422281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01091902541519327,"score_gpt":0.1950890853946181,"score_spread":0.1841700599794249,"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."}}