{"id":"W4229366107","doi":"10.1007/s10479-022-04710-7","title":"Supplier selection in closed loop pharma supply chain: a novel BWM–GAIA framework","year":2022,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Sustainable Supply Chain Management","field":"Business, Management and Accounting","cited_by":52,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Selection (genetic algorithm); Supply chain; Hierarchy; Circular economy; Supply chain management; Theory of computation; Process management; Operations research; Risk analysis (engineering); Business; Marketing; Artificial intelligence; Algorithm; Engineering; Economics","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":[0.003513271,0.001177161,0.001930255,0.001711027,0.00115743,0.00457533,0.004290403,0.003023406,0.008641665],"category_scores_gemma":[0.006578463,0.0009379101,0.001797094,0.002630312,0.002224867,0.004777861,0.004639382,0.002525327,0.001357527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001990697,"about_ca_system_score_gemma":0.002758939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006187486,"about_ca_topic_score_gemma":0.004390435,"domain_scores_codex":[0.9969192,0.001533552,0.0001077863,0.0005435925,0.0005080987,0.0003878317],"domain_scores_gemma":[0.9976324,0.001226931,0.0002806617,0.0002587157,0.0004055087,0.0001958008],"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":[0.00009935188,0.0001351495,0.001171889,0.0002034067,0.0001318254,0.0004184296,0.0001898341,0.45734,0.001185944,0.4979778,0.002986908,0.0381594],"study_design_scores_gemma":[0.00001733107,0.00005037517,0.0001586711,0.00003375913,0.00003081764,0.00006418985,0.00005771395,0.8754951,0.0001774208,0.1213908,0.00250555,0.00001824339],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008074882,0.0003735366,0.9764276,0.0007764273,0.0000721746,0.0001114845,0.0001673964,0.0001893701,0.01380707],"genre_scores_gemma":[0.6529639,0.0008524302,0.3311167,0.0003505487,0.0002298411,0.0003227769,0.0003728692,0.0001532708,0.01363758],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008641665,"threshold_uncertainty_score":0.02890921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1217885758476072,"score_gpt":0.3897344587921001,"score_spread":0.267945882944493,"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."}}