{"id":"W73105895","doi":"","title":"Life Cycle Management Strategies for Aging Engines","year":2003,"lang":"en","type":"article","venue":"Defense Technical Information Center (DTIC)","topic":"Technology Assessment and Management","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Component (thermodynamics); Life expectancy; Life extension; Perspective (graphical); Service (business); Computer science; Expectancy theory; Operations management; Risk analysis (engineering); Engineering; Business; Process management; Operations research; Economics; Marketing; Management; Sociology; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001845607,0.0001816181,0.0001537151,0.0002416626,0.00007817939,0.0001195829,0.0001801483,0.0001083478,0.00004696762],"category_scores_gemma":[0.00003282523,0.0001822575,0.00008189186,0.0001832592,0.00003293838,0.0008400892,0.00005259527,0.0001375591,0.00008852987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006697317,"about_ca_system_score_gemma":0.00001093969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.227498e-7,"about_ca_topic_score_gemma":0.000002084749,"domain_scores_codex":[0.9990124,0.000008403141,0.0003961072,0.0001065795,0.0001436094,0.0003329189],"domain_scores_gemma":[0.9995283,0.00002760615,0.00004964723,0.0002745454,0.00003686267,0.00008305171],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001063245,0.00006699082,0.0001237683,0.0004961167,0.000149328,0.000003170997,0.0001407068,0.01677077,0.00006669613,0.9305419,0.04121713,0.01041273],"study_design_scores_gemma":[0.002061875,0.00007048526,0.0009861836,0.00007963387,0.00008579639,0.00001344261,0.001493793,0.02392838,0.0005618184,0.0150403,0.9550596,0.000618707],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008203794,0.00007231573,0.8710153,0.0004022484,0.0005819147,0.000911446,0.00002116165,0.002773041,0.1160188],"genre_scores_gemma":[0.9807047,0.0000815983,0.01825805,0.0005434213,0.00001906985,0.0002545619,0.0000675589,0.00001952766,0.00005151017],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9725009,"threshold_uncertainty_score":0.7432245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0107178936929035,"score_gpt":0.2339759723820886,"score_spread":0.2232580786891851,"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."}}