{"id":"W4408543955","doi":"10.1016/j.mattod.2025.03.002","title":"Temperature-responsive multistable kirigami with reprogrammable multi-shape memory","year":2025,"lang":"en","type":"article","venue":"Materials Today","topic":"Advanced Materials and Mechanics","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Canada Research Chairs; Agency for Science, Technology and Research","keywords":"Materials science; Shape-memory alloy; Computer science; Nanotechnology; Composite material","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":[0.0002540291,0.0002981156,0.0004191237,0.00009368322,0.0001155304,0.0001760262,0.0001781982,0.0001574564,0.0004695744],"category_scores_gemma":[0.00004906521,0.0002474872,0.00003347994,0.0001891139,0.00003544863,0.0001869096,0.00006747178,0.00009631758,0.00007370755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008429576,"about_ca_system_score_gemma":0.00004644505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004222586,"about_ca_topic_score_gemma":0.0000310319,"domain_scores_codex":[0.998693,0.00004876619,0.0003357696,0.0003477138,0.0001178325,0.0004569386],"domain_scores_gemma":[0.9992753,0.00003336679,0.00005217425,0.0004771809,0.00008621928,0.00007579537],"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":[0.0001573856,0.00003131245,0.000002047468,0.0002232777,0.00004950951,0.00004024394,0.00007142628,0.002404972,0.9939968,0.000696758,0.001087716,0.001238539],"study_design_scores_gemma":[0.00100388,0.0000462334,0.00006189051,0.0001596983,0.00003055361,0.00001052844,0.0000815982,0.0007773514,0.9691343,0.0001624356,0.02822351,0.0003080736],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9886901,0.0004686795,0.004242127,0.0000729085,0.003014567,0.0007861772,0.0001496864,0.001053443,0.001522245],"genre_scores_gemma":[0.9641433,0.0001397413,0.0270388,0.0002144324,0.0001521684,0.0002706433,0.00008972971,0.000108314,0.007842856],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02713579,"threshold_uncertainty_score":0.9999977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005963872598995255,"score_gpt":0.2217085654459821,"score_spread":0.2157446928469868,"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."}}