{"id":"W1536352717","doi":"10.1109/isuma.1995.527681","title":"Soft reliability assessment of existing transmission lines","year":2002,"lang":"en","type":"article","venue":"","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro One (Canada)","funders":"","keywords":"Reliability (semiconductor); Reliability engineering; Fuzzy logic; Hermite polynomials; Margin (machine learning); Cumulant; Electric power transmission; Series (stratigraphy); Computer science; Function (biology); Membership function; Cumulative distribution function; Mathematics; Fuzzy set; Probability density function; Engineering; Statistics; Artificial intelligence; Machine learning; Mathematical analysis; Power (physics)","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":[0.00007204949,0.0000728958,0.0001026396,0.00002457458,0.0000233739,0.000005553542,0.00005528804,0.00003915871,0.0002337628],"category_scores_gemma":[0.00001293799,0.00005745569,0.00003715054,0.00006328775,0.00001297839,0.00005730797,0.000006810236,0.00008700605,0.000002223423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002613167,"about_ca_system_score_gemma":0.000002469302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007227497,"about_ca_topic_score_gemma":5.112348e-7,"domain_scores_codex":[0.9995331,0.000005205559,0.0001707941,0.00007679446,0.00008747559,0.0001266972],"domain_scores_gemma":[0.9997807,0.00002657899,0.00001365244,0.0001162329,0.00003339332,0.00002951303],"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":[0.000004937962,0.00007364795,0.03140091,0.000985914,0.00004641965,0.000008569873,0.001156619,0.1849918,0.1911167,0.002352988,0.004473922,0.5833876],"study_design_scores_gemma":[0.0003265952,0.00004416642,0.02062833,0.0001398241,0.00001511752,0.00000433951,0.0001187705,0.8781867,0.06896574,0.000559822,0.03075121,0.0002593848],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4320519,0.0002150032,0.3657029,0.00007071014,0.0007645463,0.0001397114,0.000001947114,0.0004467311,0.2006066],"genre_scores_gemma":[0.9634501,0.00003367436,0.03604022,0.000006406301,0.0001015442,0.000002780164,5.949608e-7,0.00001034085,0.0003543732],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6931949,"threshold_uncertainty_score":0.2559539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02325008324508091,"score_gpt":0.2720124486313791,"score_spread":0.2487623653862982,"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."}}