{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003845893,0.0001795154,0.0002412699,0.002191593,0.0009652644,0.0003883372,0.0006539777,0.00006702766,0.005517237],"category_scores_gemma":[0.0006577948,0.0002018523,0.00008621023,0.004607826,0.000113171,0.0009672611,0.0009541027,0.0008605103,0.0001202288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001511051,"about_ca_system_score_gemma":0.0001742364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003732282,"about_ca_topic_score_gemma":0.0004719929,"domain_scores_codex":[0.9968448,0.0001383038,0.000476736,0.000483655,0.001256725,0.0007997931],"domain_scores_gemma":[0.9984279,0.0001631892,0.00006041994,0.0003461546,0.0009755156,0.00002685983],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007731862,0.003310014,0.02342189,0.0006800311,0.0001986351,0.0001032104,0.001646133,0.307087,0.01093742,0.4415445,0.200905,0.009392915],"study_design_scores_gemma":[0.002679841,0.0002437216,0.02282889,0.0001023737,0.00002883322,0.000006089764,0.01874707,0.3480459,0.002254747,0.01103696,0.5931817,0.0008438404],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9364308,0.0002785977,0.003895041,0.0445695,0.0004092321,0.003187051,0.00004403036,0.0001369875,0.01104875],"genre_scores_gemma":[0.9907585,0.00003942709,0.0005678426,0.002197722,0.0004834212,0.0009307639,0.0001198889,0.00004790284,0.004854541],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4305075,"threshold_uncertainty_score":0.9953918,"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."}}