{"id":"W2115562146","doi":"10.1142/s0219622003000550","title":"SELECTION OF SUPPLIERS CONSIDERING THE LEARNING EFFECT AND TECHNOLOGY IMPROVEMENT","year":2003,"lang":"en","type":"article","venue":"International Journal of Information Technology & Decision Making","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Lakehead University","funders":"","keywords":"Computer science; Selection (genetic algorithm); Learning effect; Constant (computer programming); Industrial organization; Risk analysis (engineering); Operations research; Microeconomics; Economics; Artificial intelligence; Business; Engineering","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":[0.00222624,0.0005379689,0.0009874301,0.0006968204,0.0009213542,0.00215596,0.001065633,0.002022207,0.007186388],"category_scores_gemma":[0.0103672,0.0005253477,0.0005671432,0.001065935,0.0006536801,0.003155282,0.001165622,0.000874691,0.0004365025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001608969,"about_ca_system_score_gemma":0.002182534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002667831,"about_ca_topic_score_gemma":0.003369743,"domain_scores_codex":[0.9981774,0.0008646503,0.00004601221,0.0002094402,0.0003836308,0.0003188436],"domain_scores_gemma":[0.9950629,0.003481417,0.0005612499,0.0002064448,0.000420732,0.0002672584],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004439753,0.0002144096,0.01205683,0.0002397823,0.00009648546,0.001530216,0.0004939972,0.762768,0.004269842,0.1478664,0.001827259,0.06819278],"study_design_scores_gemma":[0.0001720535,0.0004378879,0.003831665,0.00007085832,0.0001055093,0.0004175722,0.0007477378,0.8812796,0.002816774,0.1042838,0.005753867,0.00008257768],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5311938,0.001157447,0.4095488,0.003388707,0.00006078989,0.0003212653,0.0001449257,0.0001166259,0.0540676],"genre_scores_gemma":[0.9798337,0.0004285512,0.01404267,0.0001036404,0.00003478152,0.00005461297,0.00003200889,0.00001170521,0.00545833],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007186388,"threshold_uncertainty_score":0.02404082,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005642954173696579,"score_gpt":0.2426557222926437,"score_spread":0.2370127681189471,"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."}}