{"id":"W1969004393","doi":"10.1016/j.renene.2012.02.030","title":"Opportunistic maintenance for wind farms considering multi-level imperfect maintenance thresholds","year":2012,"lang":"en","type":"article","venue":"Renewable Energy","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":190,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Corrective maintenance; Maintenance actions; Preventive maintenance; Wind power; Proactive maintenance; Imperfect; Reliability engineering; Turbine; Predictive maintenance; Optimal maintenance; Risk analysis (engineering); Component (thermodynamics); Planned maintenance; Computer science; Engineering; Business","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003744673,0.0003277021,0.0003667692,0.0001236074,0.0001441104,0.0000498462,0.0002031596,0.0001885913,0.00006122609],"category_scores_gemma":[0.0002327051,0.0003062383,0.0001456979,0.0002488636,0.00009202337,0.0002927718,0.00005063329,0.0001104988,0.000009622765],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002215524,"about_ca_system_score_gemma":0.00005596942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002841114,"about_ca_topic_score_gemma":0.0002153867,"domain_scores_codex":[0.9980934,0.0000252029,0.0004050575,0.000314335,0.0001507506,0.001011311],"domain_scores_gemma":[0.9989526,0.0001359705,0.00007787593,0.000446515,0.0001285988,0.0002584685],"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":[0.00002534548,0.00005486489,0.0005631146,0.0001352213,0.00005162436,0.000003826059,0.00009820998,0.978049,0.007807308,0.004145594,0.006535593,0.002530338],"study_design_scores_gemma":[0.001856538,0.00008349725,0.0006123772,0.000250069,0.00006257579,0.00008285353,0.0002377028,0.7913837,0.01773266,0.001368102,0.1853721,0.0009578199],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006441096,0.0008117676,0.9855896,0.00007681146,0.001301502,0.0002773729,0.00008346094,0.0003974513,0.005020891],"genre_scores_gemma":[0.9553997,0.0007863704,0.03564845,0.0002133383,0.0002944049,0.0001416227,0.0000747575,0.0001142355,0.007327152],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9499412,"threshold_uncertainty_score":0.999939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03896343840959492,"score_gpt":0.2386543358455003,"score_spread":0.1996908974359054,"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."}}