{"id":"W4400470332","doi":"10.20944/preprints202407.0437.v1","title":"Adapting Procurement Practices to Disruptive Events: Lessons from Recent Supply Chain Disruptions","year":2024,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Supply Chain Resilience and Risk Management","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Supply chain; Business; Procurement; Supply chain management; Process management; Supply chain risk management; Resilience (materials science); Contingency; Contingency theory; Competitive advantage; Bespoke; Diversification (marketing strategy); Knowledge management; Marketing; Service management; Computer science","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":[0.00562916,0.0003938263,0.0003516897,0.001179445,0.003001779,0.00429588,0.001437894,0.001449684,0.001508138],"category_scores_gemma":[0.01284377,0.0003277152,0.0003439703,0.002050546,0.004026226,0.004435447,0.003408568,0.001916662,0.0003303489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003733392,"about_ca_system_score_gemma":0.003043254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007979029,"about_ca_topic_score_gemma":0.01540895,"domain_scores_codex":[0.9970297,0.001573007,0.0001285087,0.0002316821,0.000560569,0.0004765684],"domain_scores_gemma":[0.9908156,0.004656636,0.001328397,0.0009515462,0.001565058,0.0006826606],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004015663,0.0006131792,0.1566461,0.001432205,0.0001715868,0.008299318,0.4980814,0.01405168,0.005308588,0.03352156,0.008918992,0.2725538],"study_design_scores_gemma":[0.00003101432,0.0005807424,0.1112041,0.001213311,0.00007535492,0.002924798,0.7048115,0.004489945,0.004794701,0.03279998,0.1368551,0.0002194657],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.9600333,0.002614642,0.01152802,0.009969722,0.0001472405,0.0000678264,0.0001112346,0.00007295778,0.01545503],"genre_scores_gemma":[0.9940979,0.001917024,0.002316787,0.0005097638,0.0000349614,0.00001361065,0.00006030819,0.00002819099,0.00102142],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.007979029,"threshold_uncertainty_score":0.02977026,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1579940184730523,"score_gpt":0.387923116966346,"score_spread":0.2299290984932937,"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."}}