{"id":"W1979331707","doi":"10.4028/www.scientific.net/ast.54.82","title":"Design and Optimization of IPMC for Biomedical Applications","year":2008,"lang":"en","type":"article","venue":"Advances in science and technology","topic":"Dielectric materials and actuators","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Materials science; Actuator; Deflection (physics); Composite material; Nafion; Composite number; Electroactive polymers; Voltage; Artificial muscle; Polymer; Bending; Saturation (graph theory); Ionic bonding; Electrode; Computer science; Classical mechanics; Ion; Electrical 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":[0.00009395839,0.00002592819,0.00005517447,0.0001760595,0.00003754694,0.000002343081,0.00006217116,0.0000285075,0.000001038497],"category_scores_gemma":[0.0000395415,0.00002275032,0.000001544124,0.000531438,0.0004676524,0.0001127629,0.00001114905,0.00001598701,8.05396e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005533676,"about_ca_system_score_gemma":0.00001359071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":2.523587e-7,"about_ca_topic_score_gemma":1.844301e-7,"domain_scores_codex":[0.9997394,9.408971e-7,0.00006858674,0.0000738013,0.00003872902,0.00007854991],"domain_scores_gemma":[0.9998875,0.00002348967,0.00001142167,0.00004261759,0.00002354617,0.00001139796],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000976367,0.00005911119,0.0009525703,0.0001737377,0.000004202959,0.000001844669,0.0002026858,0.04863026,0.1023087,0.04239986,0.00005142002,0.8052058],"study_design_scores_gemma":[0.0008807913,0.0003907233,0.0001482117,0.00004186374,0.000007558442,0.00006738577,0.0001605172,0.6252197,0.2906925,0.04140164,0.04067606,0.0003130643],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04510077,0.001595209,0.952881,0.00006101144,0.00003261754,0.0001647402,9.08543e-7,0.00004776956,0.0001160286],"genre_scores_gemma":[0.9113182,0.003719612,0.08489195,0.00000389195,0.000003878936,0.00005896264,3.028052e-7,0.000001910499,0.000001284368],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.867989,"threshold_uncertainty_score":0.1723085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006391295447582823,"score_gpt":0.2372601361370039,"score_spread":0.2308688406894211,"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."}}