{"id":"W2320828504","doi":"10.1177/1748006x13477008","title":"An efficient method for the estimation of parameters of stochastic gamma process from noisy degradation measurements","year":2013,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Monte Carlo method; Gamma process; Sizing; Degradation (telecommunications); Computer science; Process (computing); Particle filter; Stochastic process; Noise (video); Algorithm; Filter (signal processing); Mathematics; Statistics; Artificial intelligence","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":[0.001719241,0.0001251706,0.0003497423,0.00006804569,0.00005310803,0.00001039923,0.0002418338,0.0001040968,0.000002642443],"category_scores_gemma":[0.002374977,0.00007466658,0.0001466259,0.000201999,0.0001500502,0.000268224,0.00001699524,0.0001784328,5.586532e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005188305,"about_ca_system_score_gemma":0.00004068045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005337653,"about_ca_topic_score_gemma":8.553528e-7,"domain_scores_codex":[0.9985052,0.00002406316,0.0008550017,0.0001182855,0.0003873968,0.0001100032],"domain_scores_gemma":[0.9976733,0.0003061167,0.0007024161,0.0001266946,0.001133596,0.00005787946],"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":[0.0001129173,0.0001129151,0.0001236958,0.0003307771,0.00004504895,4.085599e-9,0.0003323843,0.984795,0.009648449,0.0004644275,0.00001207572,0.00402225],"study_design_scores_gemma":[0.0005208356,0.0001853752,0.0008583192,0.0002709807,0.0002045875,0.000001554354,0.00031206,0.8835609,0.1102604,0.003756626,0.00000239528,0.00006591378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5011238,0.00006697337,0.4982088,0.00004498776,0.0001884987,0.0003443403,0.00001375632,0.000006031865,0.000002811527],"genre_scores_gemma":[0.9283882,0.00007133564,0.07149564,0.00000209298,0.00001777981,0.00001537232,0.000001598439,0.000007584304,3.774993e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4272644,"threshold_uncertainty_score":0.3044815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01249719114159995,"score_gpt":0.2419767773799488,"score_spread":0.2294795862383489,"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."}}