{"id":"W2613210738","doi":"10.1007/s10479-017-2507-2","title":"On component commonality for periodic review assemble-to-order systems","year":2017,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Component (thermodynamics); Mathematical optimization; Stock (firearms); Computer science; Order (exchange); Regular polygon; Base (topology); Operations research; Mathematics; Economics; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01153555,0.001286134,0.002607996,0.004648522,0.00267599,0.00564569,0.002847763,0.001896934,0.006115946],"category_scores_gemma":[0.07368987,0.001209792,0.003692757,0.004780233,0.005259008,0.01139999,0.005592179,0.004047347,0.0006711768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002870958,"about_ca_system_score_gemma":0.002520395,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004547182,"about_ca_topic_score_gemma":0.002601063,"domain_scores_codex":[0.9893264,0.002879655,0.00111409,0.00207112,0.003035642,0.001573181],"domain_scores_gemma":[0.9267589,0.04636625,0.005563279,0.01279211,0.006930486,0.001589],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002900655,0.0001333036,0.00432475,0.0001286669,0.000135718,0.000309138,0.0009163585,0.06719131,0.001686718,0.8841186,0.001879611,0.03888575],"study_design_scores_gemma":[0.00003575338,0.0001479598,0.001775605,0.0000844501,0.000111965,0.0002404333,0.0002807063,0.2626793,0.001306202,0.7305329,0.002739379,0.00006539186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1833626,0.0009733876,0.7935568,0.001229121,0.0001469156,0.0002391088,0.0003640373,0.0003651358,0.01976285],"genre_scores_gemma":[0.9417647,0.0007522399,0.05235377,0.0001741329,0.000294488,0.0001627748,0.0004021804,0.000185811,0.003909912],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01153555,"threshold_uncertainty_score":0.06100655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4354616218522958,"score_gpt":0.4860514672669335,"score_spread":0.05058984541463768,"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."}}