{"id":"W4378805515","doi":"10.1109/sieds58326.2023.10137798","title":"Uncovering the Most Vulnerable in Times of Crisis: Analyzing Procurement Capacity Index with Multi-Criteria Decision-Analysis","year":2023,"lang":"en","type":"article","venue":"","topic":"Supply Chain Resilience and Risk Management","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Procurement; Index (typography); Call for bids; Context (archaeology); Business; Revenue; Local government; Operations management; Actuarial science; Operations research; Finance; Economics; Computer science; Marketing; Engineering; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01071982,0.00175701,0.001636694,0.008498034,0.0009274415,0.004918614,0.001420243,0.001528718,0.002484378],"category_scores_gemma":[0.01538037,0.0005570472,0.002189097,0.005095944,0.00102634,0.001803442,0.002130561,0.001591055,0.0001863821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003335084,"about_ca_system_score_gemma":0.004224045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01084513,"about_ca_topic_score_gemma":0.009280492,"domain_scores_codex":[0.9949421,0.002871292,0.0003214975,0.0004671133,0.000847565,0.0005503504],"domain_scores_gemma":[0.9881368,0.00864199,0.0009529604,0.0002272687,0.001501012,0.0005399153],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004858848,0.0008121441,0.09069514,0.001496435,0.0009739261,0.0009810876,0.001237879,0.8161688,0.002340948,0.01194213,0.003490299,0.06937518],"study_design_scores_gemma":[0.00003309224,0.0002419948,0.01296555,0.0001731507,0.0001348854,0.00005464278,0.002088175,0.9728096,0.0007780467,0.009322538,0.001322789,0.00007545299],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7098966,0.001668206,0.270384,0.001742692,0.0001326193,0.001664766,0.003085999,0.0003169515,0.01110813],"genre_scores_gemma":[0.9090896,0.0003698282,0.08751479,0.0001251512,0.0000378061,0.0006232532,0.001499112,0.00003344602,0.0007070283],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01084513,"threshold_uncertainty_score":0.05669248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02134837056590708,"score_gpt":0.2621532959990931,"score_spread":0.240804925433186,"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."}}