{"id":"W4391046230","doi":"10.1038/s41467-024-44995-9","title":"A magnetic multi-layer soft robot for on-demand targeted adhesion","year":2024,"lang":"en","type":"article","venue":"Nature Communications","topic":"Micro and Nano Robotics","field":"Physics and Astronomy","cited_by":123,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; Shanghai Municipal Education Commission; National Natural Science Foundation of China","keywords":"Robot; Adhesion; Reconfigurability; Materials science; Nanotechnology; Adhesive; Layer (electronics); Computer science; Soft materials; Composite material; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001055638,0.0001286118,0.0001199343,0.00007676393,0.0002526128,0.00007389013,0.0005049614,0.000125887,0.0001297547],"category_scores_gemma":[0.00001845813,0.0001117717,0.0001273432,0.0001919782,0.00004272734,0.00005768774,0.0001205281,0.0005800434,0.0001143579],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002048888,"about_ca_system_score_gemma":0.00006267583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009436474,"about_ca_topic_score_gemma":0.00001695532,"domain_scores_codex":[0.9993717,0.00004553711,0.0001616817,0.000182114,0.00007079129,0.000168183],"domain_scores_gemma":[0.9985826,0.0003545371,0.00003159217,0.0008987613,0.00007995898,0.0000525443],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006725343,0.001781002,0.002948099,0.0001595409,0.0003808714,0.000001888639,0.001439177,0.001137263,0.09626152,0.5491334,0.2329492,0.1137408],"study_design_scores_gemma":[0.001315053,0.0002126694,0.004183941,0.0002661625,0.0002321994,0.000001738052,0.0001788086,0.09649239,0.008892153,0.006247474,0.8813685,0.0006089644],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02571838,0.2989894,0.5654781,0.04672664,0.005798424,0.00813113,0.002880695,0.001783286,0.04449389],"genre_scores_gemma":[0.9612291,0.00007864666,0.03613914,0.0001949803,0.0001397218,0.00009466619,0.0002664815,0.00002834562,0.00182896],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9355107,"threshold_uncertainty_score":0.4557918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02947273483837863,"score_gpt":0.3202247721785156,"score_spread":0.2907520373401369,"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."}}