{"id":"W4413786671","doi":"10.1177/02783649251364287","title":"Configuration identification of on-demand variable stiffness strain-limiting layers in zig-zag soft pneumatic actuators using deep learning methods","year":2025,"lang":"en","type":"article","venue":"The International Journal of Robotics Research","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Faculty of Medicine, University of British Columbia","keywords":"Stiffness; Actuator; Soft robotics; Limiting; Variable (mathematics); Identification (biology); Strain (injury); Control theory (sociology); Zigzag; Control engineering; Artificial intelligence; Computer science; Engineering; Structural engineering; Mechanical engineering; Materials science; Mathematics; Mathematical analysis; Geometry; Control (management)","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":[0.0003551021,0.0009643395,0.0005252658,0.000391837,0.0002035319,0.0005717747,0.000963742,0.001046494,0.001281302],"category_scores_gemma":[0.0008794537,0.0005949275,0.0006138255,0.000282379,0.0003985509,0.0006470103,0.0005998565,0.0008762877,0.0003751924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000472193,"about_ca_system_score_gemma":0.0008632145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004747369,"about_ca_topic_score_gemma":0.006044467,"domain_scores_codex":[0.9998744,0.00002083947,0.000007519344,0.0000429963,0.00002739626,0.00002681344],"domain_scores_gemma":[0.9996346,0.0001695951,0.00006487077,0.00003628662,0.00007057677,0.00002412213],"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":[0.0000737732,0.00005825347,0.001060109,0.00008211799,0.0000420789,0.0001033023,0.00003765893,0.8893811,0.00760878,0.001072066,0.0006779957,0.09980291],"study_design_scores_gemma":[0.000001354705,0.000007089853,0.00007609694,0.000002467663,0.000001881344,0.000005169575,0.000002336771,0.9989134,0.0006433794,0.0002843441,0.00006060454,0.000001788151],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08129366,0.0003476796,0.9143056,0.000136823,0.00003430794,0.00004303767,0.000108068,0.001485946,0.002244931],"genre_scores_gemma":[0.8495448,0.0001690995,0.1456695,0.0001495252,0.00002063163,0.000107024,0.0003388367,0.00008689863,0.003913594],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004747369,"threshold_uncertainty_score":0.009439468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06533339589459894,"score_gpt":0.4139239664855822,"score_spread":0.3485905705909832,"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."}}