{"id":"W4412489139","doi":"10.1073/pnas.2504304122","title":"Training and retraining liquid crystal elastomer metamaterials for pluripotent functionality","year":2025,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Advanced Materials and Mechanics","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institutes of Health Research","funders":"Division of Materials Research","keywords":"Metamaterial; Elastomer; Materials science; Function (biology); Auxetics; Surface modification; Nanotechnology; Computer science; Composite material; Mechanical engineering; Optoelectronics; Engineering","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":[0.001042072,0.00007031223,0.0001497375,0.00008328004,0.0001147257,0.00001830975,0.0001697903,0.00005274577,0.000007567639],"category_scores_gemma":[0.000274701,0.00005122682,0.00003843799,0.0002046423,0.0001607123,0.0002293438,0.00004853716,0.00004676851,6.084052e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001926074,"about_ca_system_score_gemma":0.00001489192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":3.245389e-7,"about_ca_topic_score_gemma":1.350747e-8,"domain_scores_codex":[0.9992431,0.000001987857,0.0002495642,0.0001259611,0.0002677343,0.0001116158],"domain_scores_gemma":[0.9996894,0.00007200906,0.0001058832,0.000003987619,0.000112159,0.00001655491],"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":[0.00001889,0.00000381711,0.00000456664,0.0001518249,0.00001722936,4.634727e-10,0.0001160312,0.0005778786,0.9245157,0.0740859,0.0001086687,0.0003994807],"study_design_scores_gemma":[0.0001981306,0.00004514547,0.0008109408,0.0001277032,0.00002204184,0.00000250553,0.0003054579,0.003978465,0.9042488,0.08816116,0.002020394,0.0000792753],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9951894,0.0002151395,0.0004748013,0.0005154017,0.0001659522,0.000220157,0.00005155685,0.00003540347,0.003132171],"genre_scores_gemma":[0.9940491,0.00002952968,0.005696108,0.0000868178,0.0000581838,0.00002072542,1.49153e-7,0.000003717847,0.00005564792],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02026694,"threshold_uncertainty_score":0.2088969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05304956796621555,"score_gpt":0.3011119095658186,"score_spread":0.248062341599603,"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."}}