{"id":"W2151592614","doi":"10.1002/aic.11494","title":"Optimal purchasing of raw materials: A data‐driven approach","year":2008,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Raw material; Purchasing; Sequential quadratic programming; Process (computing); Quality (philosophy); Product (mathematics); Refining (metallurgy); Process engineering; Purchasing process; Coke; Computer science; Mathematical optimization; Nonlinear programming; Quadratic programming; Engineering; Mathematics; Nonlinear system; Waste management; Materials science; Operations management; Chemistry","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":[0.00563253,0.001458911,0.002687286,0.002397029,0.0007249418,0.003903603,0.003693093,0.002352692,0.0059562],"category_scores_gemma":[0.01166248,0.002555173,0.001763122,0.0029903,0.001605095,0.003930168,0.001625844,0.002255203,0.0008726041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002568299,"about_ca_system_score_gemma":0.003301198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004672189,"about_ca_topic_score_gemma":0.005026902,"domain_scores_codex":[0.9961904,0.001656467,0.0001632757,0.0005652854,0.00117401,0.0002504988],"domain_scores_gemma":[0.9918374,0.005838414,0.000493599,0.0005437254,0.00107814,0.0002086043],"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":[0.000106582,0.0001950951,0.0009986466,0.0002001141,0.00008121591,0.0001587898,0.00005852497,0.9279547,0.0009634492,0.02791386,0.001749782,0.03961923],"study_design_scores_gemma":[0.00001309309,0.00002059024,0.0000925027,0.00001093929,0.000008355571,0.00001439609,0.00001714148,0.9794863,0.0003630099,0.01934479,0.0006183105,0.00001050634],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01540773,0.0002855146,0.9784138,0.0009916113,0.00003114424,0.0002307652,0.000629319,0.0003687228,0.003641417],"genre_scores_gemma":[0.4196149,0.0004371679,0.5735199,0.0003034507,0.0000919306,0.0006916284,0.001923558,0.000262568,0.003154869],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0059562,"threshold_uncertainty_score":0.02978802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06897971071900155,"score_gpt":0.3050988430664795,"score_spread":0.2361191323474779,"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."}}