{"id":"W2157654967","doi":"10.1109/tdei.2012.6215080","title":"Electrospinning as a new method of preparing nanofilled silicone rubber composites","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Dielectrics and Electrical Insulation","topic":"Electrospun Nanofibers in Biomedical Applications","field":"Materials Science","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Silicone rubber; Materials science; Nanocomposite; Electrospinning; Composite material; Thermogravimetric analysis; Thermal stability; Dispersion (optics); Natural rubber; Economies of agglomeration; Silicone; Filler (materials); Scanning electron microscope; Polymer; Chemical 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005318221,0.0002617943,0.000393657,0.0003760297,0.0003352716,0.00005615087,0.0001875771,0.0002373108,0.0001055798],"category_scores_gemma":[0.00005695754,0.0002473901,0.0001113827,0.001723461,0.00007536006,0.0002976787,0.000004202174,0.0003302401,0.00004173715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001663077,"about_ca_system_score_gemma":0.0001489193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001993292,"about_ca_topic_score_gemma":0.000003639689,"domain_scores_codex":[0.997687,0.0001265462,0.0005792715,0.0004219,0.0004773034,0.0007079141],"domain_scores_gemma":[0.9983785,0.0005715816,0.0002290175,0.0002793779,0.0001211537,0.0004203554],"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":[0.0001316148,0.0003625621,0.0001388142,0.00001393776,0.00003274921,1.985464e-7,0.0001772129,0.0002215119,0.9443174,0.004373619,0.0001631173,0.05006728],"study_design_scores_gemma":[0.000597454,0.0007259905,0.00072855,0.00001895037,0.0001024918,0.00004115458,0.000003711163,0.01330118,0.9813245,0.002182837,0.0006919086,0.000281293],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2272006,0.0006364959,0.7707297,0.0001728263,0.0001325205,0.0003714799,0.000004314819,0.0001308251,0.0006211989],"genre_scores_gemma":[0.953501,0.0002333772,0.04546059,0.0002637905,0.0001032343,0.00006430937,0.000006452789,0.00003310965,0.0003341471],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7263004,"threshold_uncertainty_score":0.9999979,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01299277860567989,"score_gpt":0.2937799042528277,"score_spread":0.2807871256471479,"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."}}