{"id":"W4383872070","doi":"10.1021/acsnano.2c11733","title":"Micro/Nanorobotic Swarms: From Fundamentals to Functionalities","year":2023,"lang":"en","type":"review","venue":"ACS Nano","topic":"Micro and Nano Robotics","field":"Physics and Astronomy","cited_by":87,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Government of Canada; National Natural Science Foundation of China; Canada Foundation for Innovation; Ontario Research Foundation; Chinese University of Hong Kong, Shenzhen","keywords":"Nanotechnology; Leverage (statistics); Field (mathematics); Computer science; Data science; Systems engineering; Engineering; Materials science; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001261968,0.0005672401,0.00143188,0.0002028333,0.0001647975,0.0001472887,0.0004917235,0.0001761038,0.001690535],"category_scores_gemma":[0.000008376648,0.0004892754,0.0005898772,0.0004879086,0.00004336944,0.0000755138,0.0003084299,0.0002635644,0.01183316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001259389,"about_ca_system_score_gemma":0.0003194129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004731572,"about_ca_topic_score_gemma":0.000008656059,"domain_scores_codex":[0.997866,0.00009477819,0.0006966639,0.000607526,0.0002348047,0.0005001766],"domain_scores_gemma":[0.9986093,0.0003179109,0.0002639723,0.000584304,0.00005315747,0.0001713356],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001020022,0.0002376269,0.0001518236,0.004265962,0.002478004,0.00001495088,0.0003197278,0.00003572102,0.0002100772,0.004689561,0.1637815,0.8238049],"study_design_scores_gemma":[0.0001444077,0.00002576406,0.00000364688,0.003851545,0.0006529337,0.000001215688,0.00009846907,1.683869e-7,0.00009337557,0.0009027754,0.9937034,0.0005223517],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00008815034,0.9933512,0.0004994904,0.00004246796,0.001566826,0.0007899584,0.001976277,0.0001186875,0.001566957],"genre_scores_gemma":[0.00002988429,0.9478888,0.0006948886,0.000151788,0.001984244,0.0002104966,0.002980188,0.0001870509,0.04587265],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.8299218,"threshold_uncertainty_score":0.9997559,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08054102988264289,"score_gpt":0.3356548038439917,"score_spread":0.2551137739613488,"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."}}