{"id":"W3089403702","doi":"10.1002/aisy.202000148","title":"Liquid Crystal Polymer‐Based Soft Robots","year":2020,"lang":"en","type":"article","venue":"Advanced Intelligent Systems","topic":"Advanced Materials and Mechanics","field":"Engineering","cited_by":148,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Reconfigurability; Robot; Soft robotics; Adaptability; Computer science; Soft materials; Liquid crystal; Artificial intelligence; Nanotechnology; Materials science; Mechanical engineering; Control engineering; Engineering","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"],"consensus_categories":[],"category_scores_codex":[0.00005929475,0.0002851505,0.0003617443,0.00004526062,0.00005530315,0.00004101268,0.0002135827,0.00009713492,0.00009427619],"category_scores_gemma":[0.00003457098,0.0002852559,0.00008890723,0.0001711188,0.00001681352,0.0001845142,0.00003341176,0.0001360947,0.000241797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007962279,"about_ca_system_score_gemma":0.0000197893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005782595,"about_ca_topic_score_gemma":0.000001296232,"domain_scores_codex":[0.9986119,0.00002360055,0.0004544787,0.0002950939,0.0002031479,0.000411823],"domain_scores_gemma":[0.9993215,0.00003950977,0.00007078957,0.0002634051,0.00004865146,0.0002562071],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006286877,0.00001027789,0.000001204862,0.000242167,0.00001993821,0.00001120444,0.0000900331,0.5504841,0.4466624,0.001153505,0.000207957,0.001054357],"study_design_scores_gemma":[0.0002951162,0.0002776855,5.106627e-7,0.0001275532,0.00001593827,0.000005873314,0.0002481227,0.1607682,0.7707339,0.00003290378,0.06705263,0.0004416035],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01934293,0.004672171,0.9699455,0.00009098944,0.00324955,0.0004725817,0.00004746321,0.001150852,0.001027962],"genre_scores_gemma":[0.9975649,0.0001706603,0.001309574,0.0002631097,0.0003578658,0.00006261365,0.00003147138,0.0001046246,0.0001352115],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.978222,"threshold_uncertainty_score":0.9999599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01859083027134891,"score_gpt":0.2238026913514416,"score_spread":0.2052118610800927,"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."}}