{"id":"W2336496626","doi":"10.5539/mas.v10n6p74","title":"Identifying and Assessing the Risks in the Supply Chain","year":2016,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Supply Chain Resilience and Risk Management","field":"Business, Management and Accounting","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Ranking (information retrieval); Supply chain; Procurement; Supply chain risk management; Scope (computer science); Risk analysis (engineering); Risk management; Business; Index (typography); Schedule; Actuarial science; Supply chain management; Risk assessment; Computer science; Operations management; Service management; Economics; Marketing; Finance","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.00418719,0.0008970491,0.0005657476,0.004797263,0.001094523,0.003010693,0.0005185703,0.0009136695,0.001457759],"category_scores_gemma":[0.009811827,0.0003365298,0.0005694681,0.002677789,0.0008939417,0.003024608,0.001911237,0.0006401199,0.0002033203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001577573,"about_ca_system_score_gemma":0.0022809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00266749,"about_ca_topic_score_gemma":0.002253194,"domain_scores_codex":[0.9945641,0.001997037,0.0003538162,0.0002858522,0.002504113,0.000295132],"domain_scores_gemma":[0.9946833,0.00236935,0.00128476,0.0001974127,0.001268861,0.0001962504],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0003646036,0.0002762588,0.197655,0.0009539196,0.0005802079,0.00139827,0.004022415,0.4289911,0.01428333,0.05295923,0.001587116,0.2969285],"study_design_scores_gemma":[0.00003917535,0.0009314839,0.1226862,0.0006975654,0.0003995546,0.001002161,0.01402467,0.6760985,0.01444926,0.157239,0.01212812,0.0003043587],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7122331,0.001368549,0.2687188,0.0009755373,0.00003790708,0.0004341057,0.0002174322,0.0001417002,0.01587278],"genre_scores_gemma":[0.9586614,0.0005981345,0.03944593,0.00002803586,0.00001320258,0.0000755486,0.00009194191,0.000007913255,0.001077727],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004797263,"threshold_uncertainty_score":0.02214426,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04446384027664849,"score_gpt":0.2980409388909024,"score_spread":0.2535770986142539,"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."}}