{"id":"W4403042980","doi":"10.1002/adfm.202413129","title":"Self‐Propelled Morphing Matter for Small‐Scale Swimming Soft Robots","year":2024,"lang":"en","type":"article","venue":"Advanced Functional Materials","topic":"Micro and Nano Robotics","field":"Physics and Astronomy","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Division of Materials Research; University of Michigan; Michigan Center for Materials Characterization, University of Michigan; National Science Foundation","keywords":"Morphing; Materials science; Soft matter; Soft robotics; Scale (ratio); Robot; Soft materials; Nanotechnology; Composite material; Polymer science; Computer science; Artificial intelligence; Chemical engineering; Engineering; Physics","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":[0.00004409053,0.0001205319,0.0000901725,0.00009678074,0.0001207734,0.0001501884,0.0001174178,0.0001649946,0.001039731],"category_scores_gemma":[0.00008295344,0.0000621807,0.0001042318,0.00004461189,0.0002419817,0.0001403595,0.0001907688,0.0001466089,0.0002131609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001732847,"about_ca_system_score_gemma":0.0001404395,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001967697,"about_ca_topic_score_gemma":0.0003825478,"domain_scores_codex":[0.9999796,0.000002629692,0.000001265722,0.000004280474,0.000009116363,0.000003128542],"domain_scores_gemma":[0.9999568,0.000008166542,0.00001492237,0.000005053824,0.000005731782,0.000009310248],"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":[0.00002451595,0.00003133972,0.0002655212,0.0001030203,0.000005936182,0.00008943664,0.00002906844,0.009775789,0.9723426,0.006344104,0.0002162925,0.01077246],"study_design_scores_gemma":[0.00006910259,0.0006622706,0.002316526,0.00002075718,0.00001626665,0.0001759387,0.0000349199,0.3082562,0.6663733,0.003835148,0.01819373,0.00004575275],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9080218,0.0007474758,0.08193327,0.0002363085,0.0001103033,0.0000644009,0.00003914148,0.0002504127,0.008596915],"genre_scores_gemma":[0.9807247,0.000155245,0.01630817,0.00004344275,0.000009288567,0.00004926137,0.00001658939,0.00001337044,0.002680008],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001039731,"threshold_uncertainty_score":0.003478289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01087112477864411,"score_gpt":0.2262057783677055,"score_spread":0.2153346535890614,"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."}}