{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001034034,0.0001138015,0.0001746607,0.002103211,0.0001349327,0.0001462919,0.0003539443,0.0001042403,0.0001072368],"category_scores_gemma":[0.001342677,0.00008355836,0.00005663331,0.0006016655,0.0001158995,0.001205441,0.0001783988,0.000310126,0.00001423462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000717365,"about_ca_system_score_gemma":0.00002184301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000255972,"about_ca_topic_score_gemma":0.000002202225,"domain_scores_codex":[0.9986868,0.00001131161,0.0006801607,0.00007768476,0.000411348,0.0001326786],"domain_scores_gemma":[0.998068,0.0001181412,0.001055294,0.000091456,0.000661305,0.000005843943],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001669166,0.00003677017,0.079173,0.00006134061,0.0002635911,0.00001000991,0.0001100268,0.002395037,0.003114674,0.2010597,0.001378786,0.7122301],"study_design_scores_gemma":[0.007073946,0.0008076043,0.004624614,0.001366167,0.0002733799,0.0008373873,0.01331106,0.03161901,0.0406094,0.191524,0.7072096,0.0007438927],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9333435,0.0001253906,0.05848142,0.002460598,0.001305221,0.0002915808,5.366381e-7,0.00008830959,0.003903496],"genre_scores_gemma":[0.9975211,0.00003251651,0.001983304,0.0003694775,0.00006946892,0.000007237096,0.000001034986,0.000006546627,0.0000092918],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7114862,"threshold_uncertainty_score":0.3407411,"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."}}