{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002425307,0.0001391598,0.0003352703,0.0001206193,0.0001802841,0.00003992391,0.0006448387,0.0001054882,0.002431684],"category_scores_gemma":[0.0001377406,0.0001182994,0.00007748701,0.0002663157,0.0001226377,0.0002413701,0.0001707375,0.0003064275,0.00001239631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004634544,"about_ca_system_score_gemma":0.0001016748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002245707,"about_ca_topic_score_gemma":3.231517e-7,"domain_scores_codex":[0.998794,0.000024524,0.0004010364,0.0002021543,0.0003132663,0.0002650529],"domain_scores_gemma":[0.9990108,0.00004858008,0.0003040099,0.0004546703,0.00007459722,0.0001073219],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009428251,0.0002767568,0.002182263,0.00007310264,0.000322409,0.00008086683,0.0005036618,0.000512831,0.9886308,0.00003140335,0.007077613,0.0002139707],"study_design_scores_gemma":[0.0007182171,0.00003284636,0.0003142802,0.00002640178,0.0002435523,0.002636748,0.0006772778,0.001048335,0.9931844,0.00003469833,0.0008792629,0.0002039756],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9855047,0.000841175,0.005678944,0.00005687563,0.00006044445,0.00001833334,0.00003470606,0.00002547113,0.007779275],"genre_scores_gemma":[0.9816837,0.0003773475,0.01650084,0.00002988283,0.0005164563,9.42225e-7,0.00005585355,0.00002057401,0.000814356],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0108219,"threshold_uncertainty_score":0.9984802,"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."}}