{"id":"W2328128098","doi":"10.1177/1748006x15598914","title":"Selective maintenance scheduling over a finite planning horizon","year":2015,"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":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Preventive maintenance; Shutdown; Scheduling (production processes); Time horizon; Optimal maintenance; Reliability engineering; Planned maintenance; Computer science; Mathematical optimization; Operations research; Engineering; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001541039,0.0001430503,0.0003530605,0.00008409667,0.00005110857,0.00001334561,0.0001923159,0.0001415912,0.000001453226],"category_scores_gemma":[0.003630975,0.00009696149,0.0001421138,0.0003133922,0.0001575679,0.0003550228,0.00004742579,0.0004796437,2.000389e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001217794,"about_ca_system_score_gemma":0.00006767397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007246174,"about_ca_topic_score_gemma":3.074204e-7,"domain_scores_codex":[0.9987227,0.00001545297,0.0006468306,0.0001252107,0.0003216272,0.0001681467],"domain_scores_gemma":[0.9985012,0.0001139363,0.0003826144,0.00008655732,0.0007920759,0.0001236506],"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":[0.0002007366,0.00005442476,0.001875336,0.0002130468,0.00004084252,3.118968e-7,0.0005182755,0.9906417,0.001749405,0.003848301,0.0002788251,0.000578833],"study_design_scores_gemma":[0.003347248,0.001060123,0.003273182,0.00223738,0.0002993375,0.00006401313,0.001685579,0.8828053,0.07409543,0.02780436,0.002837937,0.0004900932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9156746,0.0004082015,0.08252353,0.00008380796,0.0008072014,0.0001673629,0.000009104523,0.00003043686,0.0002957828],"genre_scores_gemma":[0.9890071,0.0005929437,0.01029533,0.000004951433,0.00008282143,0.000002381614,3.2589e-7,0.000009983298,0.000004173588],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1078363,"threshold_uncertainty_score":0.4346879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009654871328553168,"score_gpt":0.215356058352515,"score_spread":0.2057011870239618,"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."}}