{"id":"W2160368919","doi":"10.1109/iis.1997.645236","title":"A competitive tendering strategy model and software system based on fuzzy set theory","year":2002,"lang":"en","type":"article","venue":"","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Procurement; Margin (machine learning); Fuzzy set; Fuzzy logic; Computer science; Set (abstract data type); Competitive advantage; Operations research; Software; Sample (material); Industrial engineering; Business; Engineering; Artificial intelligence; Machine learning; Marketing","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":[0.00238287,0.000246608,0.0003911502,0.0004107094,0.0002430103,0.0006195426,0.0005589168,0.0001048988,0.001314293],"category_scores_gemma":[0.001436924,0.0001674867,0.00009807161,0.0003917726,0.0001058018,0.0003065241,0.0001708529,0.0001765341,0.0005168528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006376561,"about_ca_system_score_gemma":0.00002849174,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007149027,"about_ca_topic_score_gemma":0.00001680393,"domain_scores_codex":[0.9964087,0.0003993828,0.0006370121,0.0007821125,0.001436257,0.0003365449],"domain_scores_gemma":[0.9950061,0.003594728,0.0001705256,0.0007800703,0.000256111,0.0001924099],"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":[0.0002647583,0.0001910553,0.001299539,0.00006208509,0.00003381821,0.000200942,0.001748422,0.2524635,0.0006491441,0.5626161,0.005965235,0.1745054],"study_design_scores_gemma":[0.0006114628,0.00006585905,0.0006905877,0.0001002864,0.000006444883,0.00001954727,0.00408684,0.980377,0.00005365089,0.01335297,0.00040555,0.0002298131],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08943541,0.0001584695,0.6649907,0.0002581447,0.0003457176,0.0005572661,0.0001511814,0.0004842665,0.2436188],"genre_scores_gemma":[0.9777958,0.000001784257,0.01910381,0.0005340632,0.00003558455,0.00001305261,0.000001665076,0.00002209138,0.002492195],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8883603,"threshold_uncertainty_score":0.9995986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2398611332819565,"score_gpt":0.3798844516459263,"score_spread":0.1400233183639697,"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."}}