{"id":"W3176035139","doi":"10.1108/k-02-2021-0161","title":"Cold chain vulnerability assessment through two-stage grey comprehensive measurement of intuitionistic fuzzy entropy","year":2021,"lang":"en","type":"article","venue":"Kybernetes","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Cold chain; Vulnerability (computing); Vulnerability assessment; Entropy (arrow of time); Computer science; Adaptability; Fuzzy logic; Reliability engineering; Data mining; Operations research; Risk analysis (engineering); Mathematics; Artificial intelligence; Computer security; Engineering; Business; Physics","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":[0.002613823,0.0009810275,0.0006613879,0.002740469,0.0006613523,0.001935447,0.0007652011,0.0006380721,0.001199748],"category_scores_gemma":[0.004857157,0.0003255333,0.001298548,0.001143161,0.001304661,0.002551718,0.001687038,0.0006511056,0.00006305536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002452229,"about_ca_system_score_gemma":0.001531866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004448042,"about_ca_topic_score_gemma":0.002942852,"domain_scores_codex":[0.9978903,0.0007483291,0.0001199981,0.0002775289,0.0007861464,0.0001776399],"domain_scores_gemma":[0.9984702,0.0007052156,0.0002861586,0.00009426622,0.0003659158,0.00007824229],"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":[0.0002336616,0.0001257639,0.01745243,0.000380861,0.000327564,0.0003802567,0.001298388,0.7974759,0.01276651,0.09314221,0.0007656812,0.07565074],"study_design_scores_gemma":[0.00001077771,0.0001226384,0.004910884,0.00003817882,0.00006423838,0.0000671286,0.0002300042,0.9604731,0.003043964,0.03046769,0.0005143147,0.0000570829],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.164101,0.0002644088,0.828326,0.0002160192,0.00002309094,0.0001834158,0.00009004391,0.0001149462,0.006681055],"genre_scores_gemma":[0.9709563,0.00009637768,0.02828563,0.00001382293,0.000008466306,0.00007836348,0.0000347753,0.000005364766,0.00052094],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004448042,"threshold_uncertainty_score":0.01779222,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2353495406968461,"score_gpt":0.4432480912243309,"score_spread":0.2078985505274848,"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."}}