{"id":"W4412254954","doi":"","title":"Exclusion, Qualification and Selection of Candidates and Tenderers in EU Procurement","year":2016,"lang":"en","type":"article","venue":"Research at the University of Copenhagen (University of Copenhagen)","topic":"Public Procurement and Policy","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institute on Governance","funders":"","keywords":"Selection (genetic algorithm); Procurement; Business; Computer science; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04096467,0.0002782561,0.001360199,0.003020914,0.004651148,0.006936733,0.001928749,0.003348203,0.01209842],"category_scores_gemma":[0.1306312,0.0004749054,0.0007196715,0.002880619,0.003890832,0.002940743,0.005302335,0.003148228,0.001400789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002175655,"about_ca_system_score_gemma":0.004821425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007946064,"about_ca_topic_score_gemma":0.01013066,"domain_scores_codex":[0.9605814,0.02105734,0.002140879,0.001766547,0.003963106,0.01049084],"domain_scores_gemma":[0.8657227,0.09076737,0.01745746,0.002776116,0.006658508,0.01661784],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.01077879,0.001702074,0.7843859,0.0004226296,0.0001890095,0.00228086,0.02170742,0.003541284,0.0008906214,0.05031658,0.01134617,0.1124386],"study_design_scores_gemma":[0.0004248807,0.001390424,0.8838996,0.000693523,0.0001651118,0.001065255,0.05190038,0.005913549,0.0009554785,0.03572766,0.01774524,0.000118912],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9818668,0.0008868248,0.001327614,0.002385593,0.00007301889,0.00008718956,0.0001069052,0.000006640433,0.01325922],"genre_scores_gemma":[0.9965509,0.00007875685,0.0001615468,0.0002323132,0.00003401407,0.00002877658,0.00008033493,0.000005871617,0.002827571],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04096467,"threshold_uncertainty_score":0.2166445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04098195667123202,"score_gpt":0.2605304758501434,"score_spread":0.2195485191789114,"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."}}