{"id":"W2440724966","doi":"10.3329/jme.v45i2.28977","title":"Mathematical Modeling for Measures of Supply Chain Flexibility","year":2016,"lang":"en","type":"article","venue":"Journal of Mechanical Engineering","topic":"Supply Chain Resilience and Risk Management","field":"Business, Management and Accounting","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Supply chain; Flexibility (engineering); Service management; Supply chain risk management; Linkage (software); Industrial organization; Production (economics); Business; Demand chain; Commodity; Supply chain management; Competitive advantage; Product (mathematics); Measure (data warehouse); Operations management; Risk analysis (engineering); Microeconomics; Computer science; Marketing; Economics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002546408,0.001365656,0.0007811457,0.001934044,0.0008957965,0.002575851,0.001978354,0.002138308,0.008791757],"category_scores_gemma":[0.00970701,0.0005585108,0.001503186,0.001945891,0.001656858,0.004640419,0.001551049,0.003002787,0.001708774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003148139,"about_ca_system_score_gemma":0.001532643,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00418257,"about_ca_topic_score_gemma":0.002604214,"domain_scores_codex":[0.9985718,0.0005158287,0.00008567389,0.0002537976,0.0004164903,0.0001563831],"domain_scores_gemma":[0.9963696,0.002168193,0.0005996711,0.0002183851,0.0005240873,0.0001200997],"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":[0.000006581664,0.00001936838,0.0003019522,0.00006316359,0.00002181156,0.00006919343,0.00008645451,0.2272678,0.0003656489,0.7640165,0.002026973,0.005754569],"study_design_scores_gemma":[0.000004557244,0.00001554201,0.0001692267,0.00004274633,0.00001106919,0.00005450992,0.00003954498,0.6240994,0.0001116409,0.3693389,0.006094665,0.0000182685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004544548,0.001420309,0.9678875,0.001636213,0.0001416246,0.00006172788,0.0002802561,0.0001394337,0.0238884],"genre_scores_gemma":[0.6941296,0.006774647,0.2392399,0.0009592882,0.0008009503,0.001283227,0.0008898093,0.0003057682,0.05561688],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008791757,"threshold_uncertainty_score":0.02941132,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03602853096189799,"score_gpt":0.2438623239882651,"score_spread":0.2078337930263671,"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."}}