{"id":"W2999236449","doi":"10.1109/tpwrs.2020.2966913","title":"Assessment of Spare Parts for System Components Using a Markov Model","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro One (Canada)","funders":"","keywords":"Spare part; Reliability engineering; Markov chain; Computer science; Markov model; Component (thermodynamics); Markov process; Engineering; Mathematics; Operations management; Statistics; Machine learning","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":[0.000639584,0.0002080713,0.0005809029,0.0001462612,0.0002258201,0.0001100591,0.0004200135,0.00008590446,0.00001276052],"category_scores_gemma":[0.00007814001,0.0001767133,0.0001634151,0.0004155593,0.00005701653,0.0002699811,0.000003465991,0.000166465,0.00001381509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001430769,"about_ca_system_score_gemma":0.00009781391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002099952,"about_ca_topic_score_gemma":0.000001956582,"domain_scores_codex":[0.9966841,0.0001373034,0.001033471,0.0005630928,0.001291917,0.0002901368],"domain_scores_gemma":[0.9976407,0.0008846502,0.0003772812,0.000410227,0.0004468198,0.0002403435],"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":[0.00007635642,0.00007147447,0.00007880656,0.0002370126,0.00004063586,0.000004346824,0.0003197513,0.9953832,0.002725583,0.0006643995,0.00007875473,0.0003196966],"study_design_scores_gemma":[0.0004737173,0.000128089,0.00004891687,0.0001915013,0.00003743449,0.00000601058,0.001553861,0.9960393,0.00105874,0.00009801838,0.0001919244,0.0001724288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006982726,0.00003649985,0.9891738,0.00005242453,0.001877551,0.0007063753,0.0005615983,0.00008018868,0.0005288568],"genre_scores_gemma":[0.9748668,7.978876e-7,0.02485445,0.00002414205,0.00002459129,0.00008865196,0.000001343719,0.00003101298,0.0001081724],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9678841,"threshold_uncertainty_score":0.720616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2306603841396903,"score_gpt":0.4251754372255071,"score_spread":0.1945150530858168,"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."}}