{"id":"W4244149159","doi":"10.1109/61.956744","title":"A statistical approach to prediction of ZnO arrester element characteristics","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Power Delivery","topic":"Surface Roughness and Optical Measurements","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Nonlinear system; Materials science; Resistive touchscreen; Percolation (cognitive psychology); Voltage; Varistor; Fraction (chemistry); Current (fluid); Electronic engineering; Condensed matter physics; Electrical engineering; Physics; Engineering; Chemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004458816,0.0003813143,0.0004500544,0.0006380928,0.0003335086,0.0004388851,0.00071809,0.0007747783,0.001085825],"category_scores_gemma":[0.00355055,0.000442147,0.0004550536,0.0004728179,0.0005730257,0.0007246069,0.0002607996,0.0005081909,0.0002272799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009231058,"about_ca_system_score_gemma":0.000847101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01010286,"about_ca_topic_score_gemma":0.007548095,"domain_scores_codex":[0.9998198,0.00004088993,0.000008217388,0.0000278498,0.00007677369,0.00002651665],"domain_scores_gemma":[0.9979693,0.001414965,0.0001719108,0.0001069027,0.0002805327,0.00005638391],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005867413,0.00001064836,0.0005226455,0.000005306315,0.00000475948,0.00002228866,0.000004647459,0.9943789,0.0009495565,0.002159145,0.00006376343,0.001872597],"study_design_scores_gemma":[6.955133e-7,0.000002317695,0.00009249553,2.155685e-7,3.907743e-7,0.000001882986,4.905579e-7,0.9993402,0.0001222625,0.0004173391,0.00002089577,9.144966e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2509342,0.000194118,0.7425076,0.0003417504,0.00003418146,0.00006781981,0.0002384507,0.000826756,0.004855145],"genre_scores_gemma":[0.9665185,0.0001568714,0.03136387,0.00004841436,0.00003996927,0.00009972064,0.0001320736,0.00005175195,0.001588818],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01010286,"threshold_uncertainty_score":0.02008814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02169181652575693,"score_gpt":0.2133540880011812,"score_spread":0.1916622714754242,"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."}}