{"id":"W2156384856","doi":"10.1109/icqr2mse.2011.5976648","title":"Managing performance based logistics by balancing reliability and spare parts stocking","year":2011,"lang":"en","type":"article","venue":"","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Spare part; Reliability (semiconductor); Computer science; Service (business); Capital equipment; Reliability engineering; Metric (unit); Product (mathematics); Service provider; Operations research; Operations management; Manufacturing engineering; Business; Engineering; Marketing","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":[0.000214288,0.0001129267,0.000113498,0.00002705659,0.00006600513,0.00002088858,0.00006317579,0.00005399469,0.00007968592],"category_scores_gemma":[0.00004803344,0.0001048893,0.0000167976,0.00007549729,0.00004839542,0.0001764234,0.00002068645,0.00009919718,0.0000054299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000050152,"about_ca_system_score_gemma":0.000006209897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003303607,"about_ca_topic_score_gemma":0.000008155749,"domain_scores_codex":[0.999359,0.00001197454,0.0001663107,0.0001742537,0.00007662732,0.0002118109],"domain_scores_gemma":[0.999653,0.0000312431,0.00001952615,0.0002044643,0.0000356811,0.00005609669],"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":[0.00001307856,0.00002112861,0.02650669,0.0003494245,0.000006720014,0.000001474587,0.0003309782,0.9632865,0.000101139,0.0002179183,0.001402717,0.007762302],"study_design_scores_gemma":[0.0001417836,0.00002488043,0.003555896,0.00004522313,0.000008248256,0.000001104018,0.00007976799,0.9927974,0.002158402,0.0002553711,0.0007768829,0.000155001],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1337624,0.0001048602,0.8412211,0.00005442185,0.0001980229,0.0001896846,0.000004611542,0.0004087017,0.02405626],"genre_scores_gemma":[0.985456,0.0001553151,0.01419834,0.00006464922,0.00001268366,0.0000111507,0.000008546958,0.00001632556,0.00007695371],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8516937,"threshold_uncertainty_score":0.4277262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01288877049759091,"score_gpt":0.1797272647860098,"score_spread":0.1668384942884189,"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."}}