{"id":"W4399798468","doi":"10.1049/icp.2024.0606","title":"Hydrophobicity classification of RTV silicone rubber-coated insulators using deep learning algorithms","year":2023,"lang":"en","type":"article","venue":"IET conference proceedings.","topic":"High voltage insulation and dielectric phenomena","field":"Materials Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Silicone rubber; Materials science; Natural rubber; Computer science; Silicone; Algorithm; Composite material","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":[0.0005882154,0.0002107771,0.0003286878,0.000292879,0.0002416428,0.0001339277,0.0003042724,0.0001455704,0.0003301289],"category_scores_gemma":[0.0003809213,0.0002068153,0.00005577979,0.00142375,0.0001471904,0.0004291896,0.00009693969,0.0002171006,0.0002209356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009635008,"about_ca_system_score_gemma":0.0001222063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001236399,"about_ca_topic_score_gemma":0.000002946208,"domain_scores_codex":[0.9980872,0.00003015038,0.0005314999,0.000458454,0.0004563751,0.0004363808],"domain_scores_gemma":[0.9986763,0.00005017631,0.0004045331,0.0001281961,0.0006031597,0.0001376063],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002288251,0.00004256069,0.007001969,0.00004953018,0.000007761894,0.00000128125,0.0019438,0.000147505,0.9858896,0.003310861,0.00002218158,0.001560043],"study_design_scores_gemma":[0.000531257,0.000143569,0.02920158,0.00006744709,0.00002677311,0.000005939656,0.001266236,0.5389938,0.4273986,0.001716759,0.0002837061,0.0003642788],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942627,0.00002619343,0.002109624,0.00004945559,0.0001972283,0.0002296717,0.00000683908,0.0004455216,0.002672792],"genre_scores_gemma":[0.9988474,0.00003204348,0.0007514525,0.00002732275,0.00007564352,0.00002053634,0.0000184279,0.00002517982,0.0002019945],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.558491,"threshold_uncertainty_score":0.8433685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04773348001817045,"score_gpt":0.2821565798407242,"score_spread":0.2344230998225537,"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."}}