{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001521382,0.0001093451,0.0002284161,0.0003945605,0.0004513992,0.00002343364,0.0004253978,0.00007216482,0.02916737],"category_scores_gemma":[0.0001078642,0.00009474497,0.00004258343,0.0007283469,0.0005240535,0.001016735,0.0006726186,0.0001034595,0.001139308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001216205,"about_ca_system_score_gemma":0.0001133658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004692165,"about_ca_topic_score_gemma":0.004101804,"domain_scores_codex":[0.9987452,0.0001221873,0.0001478269,0.0002624643,0.0004522914,0.0002700549],"domain_scores_gemma":[0.998985,0.0001421223,0.0002448417,0.0001758399,0.0004161361,0.00003604354],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001207185,0.0001849245,0.002651672,0.0005770706,0.000127631,0.000009679614,0.002481645,0.000004774699,0.07411444,0.005929269,0.8993731,0.01333858],"study_design_scores_gemma":[0.002933046,0.0001233132,0.1239257,0.0002419963,0.00007770121,0.000002130588,0.007072687,0.0005855547,0.003789416,0.0001788778,0.8608266,0.0002429104],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9325253,0.0008605809,0.0003610195,0.004543844,0.00001714235,0.000696016,0.00001599859,0.00001891354,0.06096121],"genre_scores_gemma":[0.9428888,0.0003234373,0.00007423655,0.0000268383,0.00001985706,1.685998e-7,0.00000870099,0.000006865997,0.05665107],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1212741,"threshold_uncertainty_score":0.9996384,"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."}}